A New Livestream Retail Analytics Framework to Assess the Sales Impact of Emotional Displays 

At the intersection of technology and marketing, this study develops a framework to unobtrusively detect salespeople's faces and simultaneously extract six emotions: happiness, sadness, surprise, anger, fear, and disgust. The authors analyze 99,451 sales pitches on a livestream retailing platform and match them with actual sales transactions. Results reveal that each emotional display, including happiness, uniformly exhibits a negative U-shaped effect on sales over time. The maximum sales resistance appears in the middle rather than at the beginning or end of sales pitches. Taken together, the results show that in one-to-many screen-mediated communications, salespeople should sell with a straight face. In addition, the authors derive closed-form formulae for the optimal allocation of the presence of a face and emotional displays over the presentation span. In contrast to the U-shaped effects, the optimal face presence wanes at the start, gradually builds to a crescendo, and eventually ebbs. Finally, the study shows how to objectively rank salespeople and circumvent biases in performance appraisals, thereby making novel contributions to people analytics. This research integrates new types of data and methods, key theoretical insights, and important managerial implications to inform the expanding opportunity that livestream e-commerce presents to marketers to create, communicate, deliver, and capture value.

Keywords: deep learning; emotions; face detection; livestream e-commerce; salesperson effectiveness

Livestream retailing augments traditional go-to-market strategies by reaching consumers via screen-mediated sales presentations for a variety of products. Amazon Live, Facebook Live, Taobao Live, and QVC serve as prominent exemplars. This type of retailing blends technology and marketing: the technology integrates video stream broadcast platforms, electronic payment systems, and forward and reverse logistics for efficient delivery and hassle-free returns, and the marketing combines entertainment and retailing, enhances reach via influencer marketing, shortens the purchase journey to the duration of the sales presentation, and permits value capture via innovative payment plans. In a typical video sales pitch, a host (salesperson) nudges a prospective customer through the purchase funnel by building awareness of an item's features, benefits, price, and discounts, as well as instilling urgency to buy. For example, on Taobao Live, which reaches over 37 million Chinese viewers monthly, top influencer Wei Ya helped Procter & Gamble accelerate its customers' journey from awareness to purchase and Tesla generate customer leads ([19]).

The continuous stream of sales presentation videos can be captured using advanced computing capabilities ([40]; [59]). Using video footage, marketing scientists can apply artificial intelligence technologies to automatically detect a salesperson's face in each frame, extract emotional expressions, and relate them to actual customers' behavioral data —all unobtrusively at a large scale— to generate novel insights ([34]), thereby augmenting the sparse knowledge on the business impact of emotional displays.

Recent studies have investigated the effects of emotional displays on various marketing metrics. For example, [50] offer the first study to automatically extract the emotions of joy and surprise that viewers experience when watching television commercials and then relate these emotions to attention and ad avoidance behavior. Similarly, [32] examine the impact of emotional displays on sales using the Facial Action Coding System ([13]) to categorize a set of emotions (e.g., happiness, surprise, disgust) based on facial expressions of viewers watching movie trailers, compute the average watching intention for each movie trailer, and relate it to box-office revenues. They find that viewers' emotional displays of happiness positively influence both watching intentions and box-office revenues. However, although [32] incorporate prospective consumers' emotional displays into their study, the role of emotional displays on the seller side of the exchange dyad remains an unexplored cue that shapes consumers' purchases. Indeed, in their survey of the literature on emotions, [ 3], p. 184) emphasize that "much of what we do know is confined to consumer behavior, as opposed to the behavior of salespeople or marketing managers."

To our knowledge, the current study is the first to assess the sales impact of product, price, sales force, advertising, and promotion in the presence of a salesperson's face and emotional displays. Specifically, we address the following knowledge gaps:

• Do salespeople's emotions in livestream presentations impact sales? If so, to what extent?
• How do the effects of emotions vary over an item's presentation span?
• What is the optimal allocation of face presence and emotions over the presentation span?

To answer these and related questions, we collaborate with a livestream retailer that broadcasts television shows 24 hours each day of the week, deploys salespeople to deliver live sales presentations of hedonic products, receives payments by credit cards, and ships orders by mail. A typical show lasts one hour and presents about eight items. We analyze the video data consisting of 62.32 million frames over two years. To put this scale in perspective, this footage exceeds two million 30-second TV ads. Then, we apply two machine-learning algorithms: real-time face detection ([56]) and real-time emotion classification ([ 1]). Specifically, the face detection algorithm discovers the presence or absence of a face in every frame of the video, while the emotions classifier (based on a convolutional neural network with rectified linear unit activation) assigns probabilities to the facial expressions in each frame with a face. Thus, we unobtrusively extract the display of emotions of each salesperson in one-to-many screen-mediated marketing communications in consumer markets.

Next, across 99,451 sales pitches, we match the salespeople's six emotional displays —happiness, sadness, surprise, anger, fear, and disgust— to how long each item was shown, the product category to which it belongs, the number of units sold, the price charged, and whether shipping fees were waived. Finally, we extend marketing mix models on two frontiers: the inclusion of emotional displays and salespeople's effectiveness. We emphasize that the literature on marketing mix models is vast, as is the literature on emotions; however, they do not overlap. This study bridges the two distinct domains.

Our analysis of large-scale video data shows that salespeople's emotions negatively impact sales across all six emotions, including happiness. The magnitude of sales decline across all the emotions is.47%, which is more than double the free-shipping effect (.20%). Happiness constitutes more than one-third of the total sales decline. Thus, we uncover a new maxim: sell with a straight face (i.e., reduce facial expressions).

Furthermore, the level of the optimal face presence reduces over the initial 10% span, then gradually increases as the presentation progresses, and subsequently tapers down in the last 15% span. Finally, most marketing mix models ignore the role of the sales force (e.g., [ 2]; [39]), and when they do include it (e.g., [16]), the sales force variable is operationalized at an aggregate level (e.g., number of salespeople, number of calls). Consequently, companies are limited in their insights into an individual salesperson's effectiveness. By contrast, the proposed framework uses person-specific dummy variables to estimate individual salesperson's impact, yielding valuable information on salespeople's performance, which circumvents managers' cognitive biases (e.g., homophily) in recognizing excellence and identifying candidates for retraining, thus contributing to people analytics in a sales setting. Next, we describe the conceptual background needed to interpret the empirical results.

Facial expressions are social displays that a sender strategically deploys to elicit a desired response from a receiver ([11]; [15]). In other words, facial expressions are "declarations that signify our trajectory in a given social interaction, that is ... what we would like the other to do" ([15], p. 130). They are social communicative moves that serve as "tools for social influence" ([11], p. 393).

A sender may mask true intentions. The onus is, therefore, on the receiver to decipher the sender's intent. The Emotions as Social Information (EASI) model ([51], [52]; [53]) asserts that buyers scrutinize the seller's expressions in commercial exchanges in an attempt to discern the seller's strategic intentions. For example, [57] show that sellers sporting a broad smile during an encounter are perceived as less competent and that perceived incompetence is more likely to be evident among prevention-focused customers and in high-risk consumption settings. Furthermore, their field study in a crowdfunding context reveals that a project creator with a broad smile is perceived as less competent, which reduces the total amount of money pledged for a project, the total number of large-scale donations made by backers, and the average amount of money pledged per backer. Similarly, [10] find that displays of intense happiness (e.g., a broad smile) by customer-facing employees can undermine trust and reduce satisfaction with the product.

[44], [45], [46]) provides a theoretical explanation for why receivers are likely to draw certain inferences with respect to a sender's facial expression, and the EASI model offers clues on how receivers are likely to react to the cue. Salespeople's expressions elicit customers' inferences about sellers' characteristics such as competence, trustworthiness, and persuasive intent. Such inferences, in turn, impact customers' purchasing behaviors. Drawing on extant theory, Table 1 presents the seller's facial expressions, intent, consumers' inferences about sellers, and consumers' action tendency with examples. According to [53], consumers' action tendencies are to ( 1) move toward (i.e., consumers experience a positive emotional reaction toward the influence attempt and thereby seek to cooperate with the seller); ( 2) move away (i.e., consumers experience a slightly negative emotional reaction toward the influence attempt and thereby seek to temporarily ignore or avoid the sender); or ( 3) move against (i.e., consumers experience a highly negative emotional reaction toward the influence attempt and thereby seek to terminate the interaction). Thus, Table 1 explains how salespeople's emotional displays trigger buyers' inferences in one-to-many broadcast communications.

Graph

Table 1. The Implications of a Sender's Strategic Social Communicative Moves.

1 ^a[11].

2 ^b[53].

More specifically, Table 1 provides theoretical bases to interpret our empirical results. First, positive facial expression (i.e., happiness) invokes the action tendency to move against. In a competitive buyer–seller exchange setting, a seller's happiness expression engenders consumers' inference that the seller is gaining an advantage, thereby reducing the seller's trustworthiness as well as consumers' purchasing tendency ([51], [52]; [53]) — a finding consistent with [57] that a seller's broad smile results in the inference of low competence and the action tendency of reduced buying activity. Second, negative facial expressions (i.e., sadness, anger, fear, and disgust) invoke the action tendency to move away. For instance, a seller's sadness expression in a selling context invokes the consumers' inference of garnering empathy as an attempt to lower their guard, which can be off-putting to consumers, who might then either ignore or avoid the seller. Consistent with this expectation, [10]) show that the display of sadness by frontline employees undermines trust and lowers satisfaction. Last, surprise can be either a positive or negative facial expression: consumers can infer that the seller is trying to garner attention, which invokes the action tendency to either move toward or move against, depending on whether consumers believe the expression is appropriate.

The proposed framework circumvents limitations such as simulated interactions in laboratory studies, survey-based studies relying on self-reports, manual observation and coding of facial displays, small sample sizes, limited set of emotional displays, and lack of business metrics as the response variable. For example, [25] investigate the impact of service representatives' happiness expressions on subjects in lab settings (using trained student actors). [17] conduct surveys with airline passengers' assessments of flight attendants, and [27] rely on surveys completed by sales managers self-reporting their own ability to perceive their salespeople's emotions. [42] manually evaluates a small sample of bank employees' smiles in salesperson–customer service encounters. These studies lack the full spectrum of emotional displays and sales performance as the response. [26], a notable exception, seek to understand the content effects on sales. Specifically, they analyze a small sample of 275 sales pitches from Home Shopping Network and incorporate minute-by-minute cumulative sales; however, they ignore the role of facial expressions. To circumvent the aforementioned limitations, we develop a framework that unobtrusively collects nonsimulated market interactions, does not rely on self-reports, does not require manual observation or coding of facial displays, involves a large sample size, covers a broad spectrum of six emotional displays, and, most importantly, uses sales transactions as the response variable. Figure 1 presents the ten-step framework to capture and analyze the structured and unstructured data from livestream retailing.

Graph: Figure 1. Livestream retail analytics framework.

The first two pennants in Figure 1 list three steps that pertain to data capture. Transaction data contain structured information such as quantity of items sold, prices of items, duration of display, shipping cost, and product category. Video footage of salespeople's presentations offers the unstructured data. We process the video footage as follows. Each video frame is a colored image with a resolution of 480 $\times$ 360 pixels. For every second of the video footage, we select a frame, convert it to grayscale, and present it to a pretrained OpenCV frontal face detection model based on the Haar cascade algorithm ([56]). For each detected face, the grayscale frame bounded by the face's region of interest is forwarded to an emotion classification model to infer the emotional state of the salesperson by producing probabilities for happiness, sadness, surprise, anger, fear, and disgust. We classify emotional displays using a pretrained mini-Xception model developed by [ 1]). Thus, we unobtrusively extract data on whether a face exists in 62.32 million frames and the probabilities of emotional displays. The third pennant in Figure 1 uses time stamps (i.e., the start and end times of item displays) to compute the display duration. We match 25,565 distinct items across 6,065 shows to the accounting data on orders placed, selling prices, and free shipping waivers.

The fourth pennant in Figure 1 consists of dynamic time warping (step 4), dimension reduction (step 5), feature engineering (step 6), and mixed models (step 7). To understand dynamic time warping, let ${\mathrm{E}}_{\mathrm{is}}^{\mathrm{k}}(\mathrm{t})$ denote the time pattern of the emotional display $\mathrm{k},$ where $\mathrm{k}=1,...,6$ for an item i in show s. Data analysis commonly uses variable transformation such as squaring, which alters the magnitude of ${\mathrm{E}}_{\mathrm{is}}^{\mathrm{k}}(\mathrm{t})$ but keeps its x values (time) unaltered. In contrast, we transform ${\mathrm{E}}_{\mathrm{is}}^{\mathrm{k}}(\mathrm{t})$ by shifting, stretching, or shrinking the time argument, denoted by t in ${\mathrm{E}}_{\mathrm{is}}^{\mathrm{k}}(\mathrm{t})$ , but keeping its magnitude (y values) the same. This shifting, stretching, or shrinking applies to each item i in show s and emotion k. For example, Sin(t) and Cos(t) are different curves, yet if we replace t in Cos(t) by ( $\frac{\pi }{2}-\mathrm{t})$ , we shift the cosine curve along the time dimension to overlap it with the sine curve exactly. The function $\varphi (\mathrm{t})=\frac{\pi }{2}-\mathrm{t}$ is called a "warping" function that performs shifting; however, more generally, it can stretch or shrink time, differentially at various instants, to align curves more closely with each other with respect to their landmarks such as peaks, valleys, and inflection points. This process of dynamic time warping is also known as curve registration or landmark alignment. The resulting aligned curves serve as inputs for analysis rather than the raw curves ${\mathrm{E}}_{\mathrm{is}}^{\mathrm{k}}(\mathrm{t})$ . Subsequently, we present the empirical results with and without curve alignment to understand the benefits of this optional step.

Dimension reduction enables us to capture the dynamic effects of emotional displays on quantity sold. Specifically, for each item-show, the time t in ${\mathrm{E}}_{\mathrm{is}}^{\mathrm{k}}(\mathrm{t})$ spans over 30 epochs, which are defined as 1/30th of the total duration of item i displayed in show s. Consequently, we have 180 additional variables (i.e., 30 epochs for six emotions). Furthermore, we generate an additional 180 variables by including its quadratic terms and yet another 360 variables by interacting them with price and promotion. To maintain parsimony and mitigate collinearity, we extract the principal component to capture "happiness" (say, when $\mathrm{k}=1$ ) via the principal scores ${\mathrm{z}}_{\mathrm{kis}}={\sum }_{\mathrm{t}=1}^{30}{\hat{\mathrm{e}}}_{\mathrm{kt}}{\mathrm{E}}_{\mathrm{is}}^{\mathrm{k}}(\mathrm{t})$ , where ${\hat{\mathrm{e}}}_{\mathrm{k}}=({\hat{\mathrm{e}}}_{\mathrm{k},1},{\hat{\mathrm{e}}}_{\mathrm{k},2},...,{\hat{\mathrm{e}}}_{\mathrm{k},30}{)}^{\mathrm{\prime }}$ is the principal eigenvector that reduces the dimensionality from 30 epochs to the scalar ${\mathrm{z}}_{\mathrm{kis}}$ . Then, we regress the quantity sold ${\mathrm{Q}}_{\mathrm{is}}$ on the principal scores ${\mathrm{z}}_{\mathrm{kis}}$ to estimate the trajectory of sales impact of an emotional display together with its confidence intervals. For details, see the Web Appendix.

Feature engineering supplements the new features to represent the quadratic and interaction effects. To this end, we included ${\mathrm{z}}_{\mathrm{kis}}^2$ for each emotion k and interaction terms such as ${\mathrm{z}}_{\mathrm{kis}}\times {\mathrm{X}}_{\mathrm{is}}$ , where ${\mathrm{X}}_{\mathrm{is}}$ denotes a moderating variable of interest (e.g., price). To generate the outputs listed in the fifth pennant in Figure 1, we formulate a set of mixed models.

Figure 2 illustrates how marketing mix — product, price, sales force, display duration (advertising), and free shipping (promotion) — together with face presence and emotional displays (happiness, sadness, surprise, anger, fear, and disgust) affect the focal outcome representing customers' purchase behavior (i.e., sales). In Model 1, we formulate the marketing mix model with marketing mix variables, time effects, and random effects for items and shows. Model 2 extends Model 1 by incorporating the face presence and six emotional displays. Model 3 adds the quadratic effects of emotional displays. Model 4 further augments Model 3 with interactions of price and promotion with the emotional displays.

Graph: Figure 2. Modeling framework.

We investigate how the number of units of an item sold on a given show varies with the item's price, its duration of display, whether free shipping was waived, the product category to which it belongs, the salesperson who presented it, and the time effects (day effect, week effect, and year). The model specification is as follows:

$$\mathrm{Ln}({\mathrm{Q}}_{\mathrm{is}})={\beta }_1\mathrm{Ln}({\mathrm{P}}_{\mathrm{is}})+{\beta }_2\mathrm{Ln}({\mathrm{D}}_{\mathrm{is}})+{\beta }_3{\mathrm{S}}_{\mathrm{is}}+{\beta }_4{\mathrm{C}}_{\mathrm{is}}+\sum _{\mathrm{j}=1}^{22}{\lambda }_{\mathrm{j}}{\mathrm{H}}_{\mathrm{js}}+{\tau }^{\mathrm{\prime }}{\mathrm{T}}_{\mathrm{is}}+{\mu }_0+{\mu }_{1\mathrm{i}}+{\mu }_{2\mathrm{s}}+{ϵ}_{\mathrm{is}},$$

Graph

( 1)

where ${\mathrm{Q}}_{\mathrm{is}}$ denotes the quantity sold of an item i in the show s with ( $\mathrm{i},\mathrm{s})=1,\cdots ,\mathrm{N}=99,451$ ; ${\mathrm{P}}_{\mathrm{is}}$ represents the item's price in dollars; ${\mathrm{D}}_{\mathrm{is}}$ is the display duration in seconds; ${\mathrm{S}}_{\mathrm{is}}$ captures free shipping ( ${\mathrm{S}}_{\mathrm{is}}=1)$ or not $({\mathrm{S}}_{\mathrm{is}}=0)$ ; and ${\mathrm{C}}_{\mathrm{is}}=\{0,1\}$ indicate one of the two types of products (whose names are not disclosed for confidentiality). These five variables represent the proxies for the traditional marketing mix variables: product, price, sales force, marketing communications (i.e., length of ad), and promotion (i.e., free shipping). Given the log-log specification, $$ the parameters ( ${\beta }_1,{\beta }_2)$ respectively yield the price and duration elasticity, which quantifies the percentage sales impact associated with a 1% increase in price or duration. The parameters ( ${\beta }_3,{\beta }_4)$ measure the percentage change in sales due to free shipping and product category. In addition, ${\mathrm{H}}_{\mathrm{s}}$ is a $22\times 1$ dummy vector, with unity for the element j and zero elsewhere, that identifies individual salesperson j hosting the show s. The corresponding ${\lambda }_{\mathrm{j}}$ furnishes the sales lift due to various salespeople, $\mathrm{j}=1,...,22$ , relative to the baseline salesperson 23. A single salesperson owns the entire show in our data. The parameters $({\mu }_0,{\mu }_{1\mathrm{i}},{\mu }_{2\mathrm{s}},{ϵ}_{\mathrm{is}})$ represent the fixed intercept, random intercept for items, random intercept for shows, and the usual zero mean and constant variance normal error term, respectively. The random effects parsimoniously reflect the variability about the intercept ${\mu }_0$ due to heterogeneous impact of items and shows.

Time flows across 62.32 million seconds of the video footage in our analysis, and it exhibits periodicity for the seconds across days and for the days across weeks. To clarify, consider time in seconds since midnight. A total of 86,400 seconds elapse by the midnight of the next day, and then the clock resets to zero (00:00:00 hours). At 23:59:59 hours, the elapsed time is 86,399 seconds, and it is 5 seconds at 00:00:05 hour. Although the instants 23:59:59 and 00:00:05 differ by just 6 seconds, these two instants would be represented as if they are 86,394 seconds apart under a linear scale. Thus, to account for periodicity of the days and weeks, sine and cosine terms should be used as follows. Let ${\mathrm{t}}_{\mathrm{is}}$ represent the seconds of a day when an item i in show s is displayed, where the full day of 86,400 seconds equals 360˚ or $2\pi$ radians. Then the two periodic regressors for the day effect are ${\mathrm{D}}_{\mathrm{is}}={\left(\mathrm{Sin}\left[\frac{2\pi {\mathrm{t}}_{\mathrm{is}}}{86400}\right],\mathrm{Cos}\left[\frac{2\pi {\mathrm{t}}_{\mathrm{is}}}{86400}\right]\right)}^{\mathrm{\prime }}$ . Similarly, let ${\mathrm{d}}_{\mathrm{is}}$ represent the day of a week when an item i in show s is displayed, where the full week equals seven days. Then the two periodic regressors for the week effect are ${\mathrm{W}}_{\mathrm{is}}={\left(\mathrm{Sin}\left[\frac{2\pi {\mathrm{d}}_{\mathrm{is}}}{7}\right],\mathrm{Cos}\left[\frac{2\pi {\mathrm{d}}_{\mathrm{is}}}{7}\right]\right)}^{\mathrm{\prime }}$ . Because calendar years are not periodic, unlike seconds or days, let the dummy variable ${\mathrm{Y}}_{\mathrm{is}}$ indicate the years. Thus, ${\mathrm{T}}_{\mathrm{is}}=({\mathrm{Y}}_{\mathrm{is}},{\mathrm{W}}_{\mathrm{is}}^{\mathrm{\prime }},{\mathrm{D}}_{\mathrm{is}}^{\mathrm{\prime }}{)}^{\mathrm{\prime }}$ in Equation 1 includes these five regressors with the conformable parameter vector $\tau$ that constitutes the year effect, the week effect, and the day effect on item sales.

The proposed livestream retail analytics framework provides the fraction of the frames containing a face when item i was displayed in show s, which we denote by ${\mathrm{F}}_{\mathrm{is}}$ . In addition, when item i was displayed in show s, it furnishes the principal score ${\mathrm{z}}_{\mathrm{kis}}$ for happiness ( ${\mathrm{z}}_{1\mathrm{is}}$ ), sadness ( ${\mathrm{z}}_{2\mathrm{is}}$ ), surprise $({\mathrm{z}}_{3\mathrm{is}}$ ), anger $({\mathrm{z}}_{4\mathrm{is}}$ ), fear $({\mathrm{z}}_{5\mathrm{is}}$ ), and disgust ( ${\mathrm{z}}_{6\mathrm{is}}$ ). Incorporating them in Model 2, we extend the right-hand side (RHS) of Model 1 as follows:

$$\mathrm{Ln}({\mathrm{Q}}_{\mathrm{is}})={\alpha }_0{\mathrm{F}}_{\mathrm{is}}+{\alpha }_1{\mathrm{z}}_{1\mathrm{is}}+{\alpha }_2{\mathrm{z}}_{2\mathrm{is}}+{\alpha }_3{\mathrm{z}}_{3\mathrm{is}}+{\alpha }_4{\mathrm{z}}_{4\mathrm{is}}+{\alpha }_5{\mathrm{z}}_{5\mathrm{is}}+{\alpha }_6{\mathrm{z}}_{6\mathrm{is}}+\mathrm{RHS}(\mathrm{Model}1),$$

Graph

( 2)

where ${\alpha }_{\mathrm{k}}$ are the effects of face presence and six emotional displays on sales. Because the score ${\mathrm{z}}_{\mathrm{kis}}={\sum }_{\mathrm{t}=1}^{30}{\hat{\mathrm{e}}}_{\mathrm{kt}}{\mathrm{E}}_{\mathrm{is}}^{\mathrm{k}}(\mathrm{t})$ , the sales impact ${\hat{\alpha }}_{\mathrm{k}}{\hat{\mathrm{e}}}_{\mathrm{kt}}$ exhibits the trajectory over $\mathrm{t}=1,...,30$ epochs.

The effects of emotional displays may wax and wane. For example, moderate happiness may be effective, but limited or excessive happiness display may not be. To investigate such intensity effects, we extend Model 2 by incorporating the quadratic effects of facetime and emotional displays. Then, Model 3 is given by

$$\mathrm{Ln}({\mathrm{Q}}_{\mathrm{is}})={\alpha }_0{\mathrm{F}}_{\mathrm{is}}+{\gamma }_0{\mathrm{F}}_{\mathrm{is}}^2+\sum _{\mathrm{k}=1}^6{\alpha }_{\mathrm{k}}{\mathrm{z}}_{\mathrm{kis}}+\sum _{\mathrm{k}=1}^6{\gamma }_{\mathrm{k}}{\mathrm{z}}_{\mathrm{kis}}^2+\mathrm{RHS}(\mathrm{Model}1),$$

Graph

( 3)

where ${\mathrm{z}}_{\mathrm{kis}}={\sum }_{\mathrm{t}=1}^{30}{\hat{\mathrm{e}}}_{\mathrm{kt}}{\mathrm{E}}_{\mathrm{is}}^{\mathrm{k}}(\mathrm{t})$ , and ${\gamma }_{\mathrm{k}}$ represent the quadratic effects for the face presence and emotional displays, respectively. Equation 3 also includes the simple effects of face presence ( ${\alpha }_0)$ and emotions ( ${\alpha }_{\mathrm{k}})$ , the marketing mix effects, and salesperson's effectiveness, time, and fixed and random intercepts via Model 1.

We derive the optimal face presence and emotional displays over time in the Web Appendix, which shows that the optimal number of frames to devote to face presence and each emotion k in every epoch t is given by

$$\begin{array}{c}\begin{array}{cc}{\mathrm{F}}_{\mathrm{t}}^{*}=& \{\begin{array}{cc}-{\displaystyle \frac{{\alpha }_0{\mathrm{e}}_{0\mathrm{t}}}{2{\gamma }_0}}& \mathrm{if}{\alpha }_0{\mathrm{e}}_{0\mathrm{t}}>0,{\gamma }_0<0,\\ 0& \mathrm{otherwise}.\end{array}\end{array}\\ \mathrm{and}\\ \begin{array}{cc}{\mathrm{E}}_{\mathrm{kt}}^{*}=& \{\begin{array}{cc}-{\displaystyle \frac{{\alpha }_{\mathrm{k}}{\mathrm{e}}_{\mathrm{kt}}}{2{\gamma }_{\mathrm{k}}}}& \mathrm{if}{\alpha }_{\mathrm{k}}{\mathrm{e}}_{\mathrm{kt}}>0,{\gamma }_{\mathrm{k}}<0,\\ 0& \mathrm{otherwise}.\end{array}\end{array}\end{array}$$

Graph

( 4)

Thus, for every epoch t in the presentation span T, the optimal allocation of face presence is $\frac{{\mathrm{F}}_{\mathrm{t}}^{*}}{{\sum }_{\mathrm{t}}{\mathrm{F}}_{\mathrm{t}}^{*}}=\frac{{\mathrm{e}}_{0\mathrm{t}}}{{\sum }_{\mathrm{t}=1}^{\mathrm{T}}{\mathrm{e}}_{0\mathrm{t}}}$ ; and the optimal allocation for each emotional display k is $\frac{{\mathrm{E}}_{\mathrm{kt}}^{*}}{{\sum }_{\mathrm{t}}{\mathrm{E}}_{\mathrm{kt}}^{*}}=\frac{{\mathrm{e}}_{\mathrm{kt}}}{{\sum }_{\mathrm{t}=1}^{\mathrm{T}}{\mathrm{e}}_{\mathrm{kt}}}$ . To gain intuition, observe that ${\mathrm{F}}_{\mathrm{t}}^{*}\propto {\mathrm{e}}_{0\mathrm{t}}$ and ${\mathrm{E}}_{\mathrm{t}}^{*}\propto {\mathrm{e}}_{\mathrm{kt}}$ in Equation 4, which reveals that face and emotions allocation are proportional to the eigenvector weights: the larger the weight, the greater the intensity of face presence or emotional expressions.

Various factors can moderate the impact of sellers' affective displays on customers' attitudinal and behavioral outcomes. For example, some studies investigate boundary conditions from perceivers' characteristics, such as emotional receptivity ([30]) and epistemic motivation ([57]). Others examine the moderating roles of the selling context, such as store busyness (e.g., [18]). To complement, we explore the moderating role of factors under managers' control such as price and promotion. Specifically, we augment Model 3 as follows:

$$\mathrm{Ln}({\mathrm{Q}}_{\mathrm{is}})=\sum _{\mathrm{k}=1}^6{\delta }_{\mathrm{k}}{\mathrm{z}}_{\mathrm{kis}}{\mathrm{P}}_{\mathrm{is}}+\sum _{\mathrm{k}=1}^6{\omega }_{\mathrm{k}}{\mathrm{z}}_{\mathrm{kis}}{\mathrm{S}}_{\mathrm{is}}+\mathrm{RHS}(\mathrm{Model}3).$$

Graph

( 5)

In Equation 5, the free shipping effect equals ${\beta }_3+{\omega }_{\mathrm{k}}{\mathrm{z}}_{\mathrm{k}},$ which depends on the level of emotional display, ${\mathrm{z}}_{\mathrm{k}}$ . Similarly, each emotion ${\mathrm{z}}_{\mathrm{k}}$ moderates the price elasticity ${\eta }_{\mathrm{k}}={\beta }_1+{\delta }_{\mathrm{k}}{\mathrm{z}}_{\mathrm{k}}\mathrm{P}$ . The preceding discussion completes the inclusion of emotions in marketing mix models.

A livestream retailer, whose identity remains confidential, broadcasts shows 24 hours a day, seven days a week, on its own television channel and sells exclusive hedonic products in multiple product categories. The salesperson hosting the show presents information on products and encourages viewers to place orders by telephone. Each show lasts for 60 or 120 minutes, is planned weeks in advance, and contains live sales pitches of items (i.e., not scripted or prerecorded). Besides selling, the salespeople attempt to build parasocial relationships with viewers so that they feel a bond with virtual personalities analogous to those with television celebrities or news anchors ([49]).

Our direct-to-consumer retailer sells items from two product categories using 23 hosts as salespeople. The salesperson pitches multiple items during a show, and the item appears throughout the presentation span. We observe 99,451 sales pitches at an item-show level on salespeople's presence of face, their facial expressions, item prices, duration of display, shipping fee waivers, and, most importantly, actual sales as the dependent variable. Table 2 presents the descriptive statistics, and Tables 3 and 4 contain the estimation results for Models 1–4 obtained via the R package lme4.

Graph

Table 2. Descriptive Statistics.

Graph

Table 3. Sales Impact of Marketing Mix, Sales Force, and Time Effects.

Graph

Table 4. Sales Impact of Face and Emotional Displays.

[31]) apply convolutional neural networks to detect the presence of a person's face in a Kickstarter crowdfunding video and show empirically that the presence of a human face makes a difference in shaping the desired funding outcomes. But does it impact sales? If so, to what extent? Our study answers these questions. The estimate of.338 in Table 4 (Model 2) means that sales increase by.34% when a face is present, an effect common to all the hosts. For a specific salesperson, say salesperson 15, the impact of sales pitch is $(.338+.418)=.756$ , which means sales increase by.76%. This magnitude explains why the livestream retailer prefers live broadcasts even when items could have been posted on the internet in a faceless manner.

Table 4 for Model 2 partially presents the sales impact of happiness, sadness, anger, fear, and disgust. The estimates ${\hat{\alpha }}_{\mathrm{k}}$ for happiness (–.033), sadness (–.003), surprise (–.001), anger (–.033), fear (–.005), and disgust (–.012) are uniformly negative and statistically significant for all emotions except surprise. Thus, we conclude that emotional displays decrease sales.

We present the dynamic pattern of emotional displays with (see Figure 3) and without (see Figure 4) dynamic time warping. These dynamic patterns emerge from the elements of the eigenvector ${\hat{\mathrm{e}}}_{\mathrm{kt}}$ across the 30 epochs $\mathrm{t}=1,...,30$ . For clarity, Figures 3 and 4 present the epochs on the unit interval. The elements of the eigenvector ${\hat{\mathrm{e}}}_{\mathrm{kt}}$ , together with the estimates ${\hat{\alpha }}_{\mathrm{k}}$ , yield the total sales impact of emotions given by ${\hat{\mathrm{a}}}_{\mathrm{kt}}={\hat{\alpha }}_{\mathrm{k}}{\hat{\mathrm{e}}}_{\mathrm{kt}}$ . Summing across all the epochs, the sales impact of emotional displays are as follows: happiness (–.18%), sadness (–.02%), surprise (–.004%), anger (–.18%), fear (–.03%), and disgust (–.06%). Happiness and anger induce the largest sales decline; surprise the smallest. Summing across these emotions, the magnitude of total sales decline (.47%) is more than twice the free-shipping effect (.198%). Happiness contributes more than one-third to the total sales decline.

Graph: Figure 3. Time-varying sales impact of emotional displays with dynamic time warping.

Graph: Figure 4. Time-varying sales impact of emotional displays without dynamic time warping.

What accounts for the negative sales impact? As discussed in the "Conceptual Background" section, sellers' emotional displays trigger buyers' inference and action tendencies. Specifically, Table 1 shows that positive facial expressions such as happiness negatively impact sales because consumers infer that the seller is gaining an advantage at their expense, thereby reducing sellers' trustworthiness and, in turn, buyers' tendency to purchase ([51], [52]; [53]). This expectation corroborates [57]) findings, which show that a seller's broad smile results in a buyer's inference of a seller's low competence and reduces buying activity. A similar situation occurs with politicians sporting a "permasmile" (i.e., maintaining a smile for an extended period of time); they are not perceived as genuine, which induces distrust and leads to lost votes ([60]). As for negative facial expressions (i.e., sadness, anger, fear, and disgust), they invoke the action tendency to move away, which corroborates [10]) finding that frontline employees' displays of sadness undermine trust and reduce satisfaction. Last, surprise can be either positive or negative, and it results in an insignificant effect on sales. Thus, this large-scale evidence supports recent studies ([10]; [57]), and so we caution that emotional displays are bad for livestream retailing business.

Over an item's presentation span, the magnitude of sales impact ${\hat{\mathrm{a}}}_{\mathrm{kt}}$ builds up, attains a maximum in the middle, and recedes toward the end. Across the six emotions, this dynamic pattern holds uniformly. Are the U-shaped patterns significant? Using the expressions in the Web Appendix, we plot the confidence intervals in Figures 3 and 4. We conclude that because zero does not belong in it, except for surprise, the rest of the emotional displays, including happiness, exert significantly negative effects on sales.

What accounts for the U-shaped dynamics? The literature on advertising repetition (e.g., [ 5]; [ 9]; [38]; [41]) provides a plausible interpretation. As the sales pitch progresses, the repetitiveness of facial expressions exacerbates the negative sales impact (i.e., becomes more negative) and drives it to the lowest level. After that, often due to the tedium ([ 5]) of a protracted sales pitch, viewers' attention drifts from the message-related thoughts to their own thoughts ([ 9]) of purchase consideration, namely, balancing the benefits and costs of the presented item and deciding whether to buy. Consequently, the negative effect ameliorates during purchase consideration. [38] find a similar U-shaped pattern for the effectiveness of television commercials (see their Figures 4 and 9 for chocolate and cereal brands, respectively).

Graph: Figure 9. Optimal face allocation.

Model 3 specifies the quadratic effects to explore whether emotional displays can be optimized. Table 4 shows that the conditions ${\hat{\alpha }}_{\mathrm{k}}>0$ and ${\hat{\gamma }}_{\mathrm{k}}<0$ are not satisfied by happiness, sadness, anger, fear, and disgust. Consequently, their resulting optimal level ${\mathrm{E}}_{\mathrm{kt}}^{*}=0$ according to Equation 4. Although surprise satisfies the conditions ${\hat{\alpha }}_3>0$ and ${\hat{\gamma }}_3<0$ , the salesperson cannot express only surprise throughout the presentation in the absence of other emotions; thus, this corner solution does not seem practically useful. In contrast, the face presence satisfies the conditions ${\hat{\alpha }}_0=3.138>>0$ and ${\hat{\gamma }}_0=-4.641<<0$ , and the optimal ${\mathrm{F}}^{*}=-\frac{3.138}{(-2\times 4.641)}=$ .34. For comparison, the average face presence in Table 2 is.19. Thus, face presence should be increased from 19% to 34% to maximize sales.

Model 4 specifies the interactions of emotional displays with free shipping and price. Table 4 shows that the estimated ${\hat{\omega }}_{\mathrm{k}}$ are not significant for all k. Hence, the main effects of emotional displays hold regardless of the shipping fee waiver. Similarly, the price interaction effects of fear ( ${\hat{\delta }}_4$ ), anger ( ${\hat{\delta }}_5$ ), and disgust ( ${\hat{\delta }}_6$ ) are not significant, thereby generalizing their main effects across various prices.

In contrast, the interaction effects of happiness ( ${\hat{\delta }}_1$ ), sadness ( ${\hat{\delta }}_2$ ), and surprise ( ${\hat{\delta }}_6$ ) are significant and negative. They moderate price elasticity: ${\eta }_{\mathrm{k}}=\frac{\mathrm{\partial }\mathrm{Ln}(\mathrm{Q})}{\mathrm{\partial }\mathrm{Ln}(\mathrm{P})}={\beta }_1+{\delta }_{\mathrm{k}}{\mathrm{z}}_{\mathrm{k}}\mathrm{P}$ . Substituting ${\hat{\delta }}_1=-.009$ for happiness from Table 4 and the average price of $110.48 from Table 2, we get price elasticity ${\eta }_1=-.77-.99{\mathrm{z}}_1$ , which means viewers become more price sensitive as the intensity of sellers' happiness increases. Why? Because the buyers suspect that the seller is gaining at their expense ([53]), and they exhibit "move against" tendencies (see Table 1). The qualitatively similar results hold for sadness and surprise. These interaction effects generalize our previous findings: emotional displays are bad for business.

According to the log-linear specification, the estimated coefficient of.386 (see Model 2 in Table 3) means that, ceteris paribus, a product from category 1 sells 1.47 ( $=\mathrm{Exp}(.386))$ times more than a product from category 2. The estimated price elasticity equals –.765 (see Model 2 in Table 3), which means a 10% increase in price corresponds to a 7.65% decrease in sales. Similarly, the estimated display duration elasticity equals.626 (see Model 2 in Table 3), which means a 10% increase in display duration corresponds to a 6.26% increase in sales, which is about 2 to 6 times larger than advertising elasticity (see [47]). The free shipping increases the quantity sold by.198% (see Model 2 in Table 3). Using the average price of $110.48 and the average quantity of 69.64 (see Table 2), the shipping waiver increases revenues by $15.23 $\left(=\mathrm{\$}110.48\times {\displaystyle \frac{.198}{100}\times 69.64}\right)$ and is profitable when the shipping costs are below $16.

Human biases affect the performance appraisal process (e.g., pay, bonus, advancement rate, prestige). We propose that to mitigate these biases, salespeople should be ranked on their individual effectiveness (objective attribution) rather than average sales (naïve attribution). Model 1 facilitates the estimation of the effectiveness of an individual salesperson by controlling for prices, duration, free shipping, time of day, and week. Table 3 reports the estimates of percentage sales increase for an individual salesperson relative to the group average based on effects coding of the dummy variables (see [22]). Consider the estimate of –.488 for salesperson 1 from Model 1 in Table 3. That estimate means salesperson 1's performance is.488% below the group average. Similarly, salesperson 6's performance is.083% above the group average. These estimated effects are not affected by human cognitive biases.

Graph: Figure 5. Salesperson performance appraisal.

We compare the salesperson's performance rank based on the naïve versus objective attributions. Panel A in Figure 5 shows the ranking of 23 salespeople based on the average sales, which ignores the effects of prices, duration, free shipping, and time of day and week. In contrast, Panel B shows the ranking of the same 23 salespeople based on their individual effectiveness. The top and the bottom three salespeople remain the same under both metrics, thereby showing that the ranking attains convergent validity by identifying the same set of best and worst performers. However, the majority of salespeople (∼75%) reside in the middle, where the rank ordering differs across metrics. Thus, the objective attribution based on salesperson's effectiveness after controlling for prices, duration, free shipping, and time of day and week should guide supervisors in more objectively selecting salespeople for rewards, recognition, and retraining.

Figures 6 and 7 depict the sales impact of emotional displays based on the full model (Model 4). In these figures, a low (high) price refers to the 25th (75th) percentile of the price distribution. First, emotional displays decrease sales. This finding holds uniformly for negative and positive emotions. Because $\mathrm{Ln}(\mathrm{Q})$ serves as the dependent variable, the marginal change in it equals $\mathrm{\Delta }\mathrm{Q}/\mathrm{Q}$ , which represents "percentage change in sales." Thus, a marginal increase in emotional display corresponds to a sales decline that ranges from.004% to.18%. Across the six emotions, the magnitude of the total sales decline (.47%) is more than double the free-shipping effect (.20%). Second, because the tangent to the curves in Figures 6 and 7 becomes steeper as the intensity of emotional display increases, the sales decline accelerates. In other words, the sales decline increases at an increasing rate. Thus, not displaying emotions emerges as the optimal course of action. So, salespeople should sell with a neutral face, although how consumers interpret "neutral" depends on the sellers' gender, facial morphology, and contextual factors (e.g., [23]). Finally, the parallel curves in Figures 6 and 7 reveal the modest magnitude of moderation effects: sales decrease as price increases or promotion decreases (see the dashed curves).

Graph: Figure 6. Emotional displays by price interactions.

Graph: Figure 7. Emotional display by free shipping interactions.

In the week effect, sine and cosine variables jointly identify the sales variations across days of the week. The cosine variable differentiates the first half of the week (Monday to noon Thursday) from the second half of the week (noon Thursday to Sunday). The sine variable differentiates the middle of the week (9 p.m. Tuesday to 9 p.m. Friday) from the end of the week (9 p.m. Friday to 9 a.m. Tuesday). Similarly, in the day effect, the sine and cosine variables identify the sales variations across hours of the day. Specifically, the cosine variable differentiates post meridiem (p.m.) from ante meridiem (a.m.), while the sine variable captures the rhythms across the late evening (6 p.m. to midnight) through the night hours (midnight to 6 a.m.) to the morning hours (6 a.m. to noon) and the afternoon (noon to 6 p.m.). Because the empirical results indicate that the cosine variable is less important than the sine variable, the a.m./p.m. distinction is not critical. As expected, sales occur 24 hours a day, including the nights; peak during the day; and are larger during the weekends.

Figure 8 presents the relative contribution of marketing mix and nonmarketing variables: the former contributes 71%, whereas the latter accounts for 29% of the total ${\mathrm{R}}^2=80\text{\%}$ . The time of day, the day of the week, and the week of the year explain 20%. Emotional displays and face presence further explain 9% of the explained variance. Thus, nonmarketing variables boost explanatory power.

Graph: Figure 8. Variable importance.

A glance across the columns in Table 3 indicates a remarkable robustness. The columns reveal the estimated marketing mix effects in the presence of various operationalizations of emotional displays. For example, across Models 1–4 the price elasticity varies from –.76 to –.77, and the duration elasticity ranges from.55 to.67. Likewise, shipping and product estimates are (.22,.41), (.20,.39), (.19,.36), and (.19,.36) across Models 1–4, respectively. Salesforce effectiveness across the four models is also stable; for example, the percentage sales increase due to salesperson 15 hovers around.42. Even the rhythms of daily and weekly sales deviate only marginally. These results hold even when we replaced the static face variable ${\mathrm{F}}_{\mathrm{is}}$ in Equation 3 with the dynamic component ${\mathrm{z}}_{0,\mathrm{is}}={\sum }_{\mathrm{t}=1}^{30}{\hat{\mathrm{e}}}_{0\mathrm{t}}{\mathrm{F}}_{\mathrm{is},\mathrm{t}}$ across the epochs $\mathrm{t}=1,\cdots ,30$ . Furthermore, we tested for heterogeneous effects of emotional displays and found that the effects were homogeneous across the two product categories (see Figures 6 and 7). Thus, the broad robustness —for all the variables and across all the models— enhances confidence in these results.

We also analyzed data by splitting the presentation span of an item i displayed in a show s into three time segments. We discovered V-shaped effects across the beginning, middle, and end of the presentation span for all the six emotions, including happiness. We then extended this analysis tenfold to 30 epochs and found that not only does the parameter stability hold in both analyses, but also qualitatively similar results persist: negative U-shaped effects of emotions over the presentation span. Furthermore, the average variance inflation factor across all independent variables was 1.66, ranging from 1.01 to 5.17, which is far below 10 and thus rules out multicollinearity concerns.

To our knowledge, this study marks the first time dynamic time warping appears in marketing. To further assess robustness, we reestimated Model 2 without dynamic time warping. As mentioned previously, dynamic time warping aligns landmarks such as the peaks, valleys, and inflections of the raw emotional curves ${\mathrm{E}}_{\mathrm{is}}^{\mathrm{k}}(\mathrm{t})$ . Such landmark alignment homogenizes the timing of peaks, valleys, and inflections in ${\mathrm{E}}_{\mathrm{is}}^{\mathrm{k}}(\mathrm{t})$ . Consequently, the estimated trajectories, ${\hat{\mathrm{a}}}_{\mathrm{kt}}$ , in Figure 3 are smoother than those in Figure 4 without landmark alignments. More importantly, the overall pattern remains the same: the sales impact ${\hat{\mathrm{a}}}_{\mathrm{kt}}$ is negative, U-shaped, and similar across the six emotions. In summary, the U-shaped patterns as well as other results are robust. We close this section by comparing the performance of models on multiple metrics.

Which one of the four models is the best? Although the adjusted ${\mathrm{R}}^2$ of about 80% is remarkable, especially given 99,451 sales pitches, it does not discriminate among the four models as the log-likelihood, Akaike information criterion, and Bayesian information criterion do. Therefore, we compared the models using these metrics and present the results in Table 5. Specifically, Models 3 and 4 dominate Models 1 and 2 on all the metrics. The Bayesian information criterion selects Model 3, whereas both the other metrics (log-likelihood and Akaike information criterion) indicate that Model 4 outperforms the rest. We used Model 4 to plot Figures 6 and 7. We next discuss the implications of these findings.

Graph

Table 5. Models Comparison.

This research offers important insights into livestream retailing by addressing two foundational questions identified in this special issue dedicated to understanding the interface of technology and marketing: ( 1) How can managers use new types of data to improve marketing decision making? and ( 2) What new methods can deliver the best consumer insights to improve marketing strategy? To address the first question, the second pennant in Figure 1 captures the new type of data available from streaming videos of sales presentations, which can identify a large-scale, unobtrusive, and comprehensive set of emotions. To address the second question, the fourth pennant in Figure 1 contributes the new methods to create six emotional trajectories via functional principal components analysis and dynamic time warping to align them. Incorporating them as quadratic and moderating variables, we then assess the value of emotional displays. Building on Models 1–4, we discuss the following theoretical and managerial contributions.

Livestream e-commerce, which features hosts promoting and selling goods and services in real time via screen-mediated sales presentations, represents an emerging opportunity for marketers to create, deliver, and communicate content so as to monetize in ways not possible previously. Specifically, marketers can, first, reach customers via new channels such as social messaging apps (Facebook, WeChat), livestreaming services (e.g., Twitch), and internet platforms (e.g., Taobao Live) that integrate shopping and entertainment. Second, these technology platforms facilitate purchases from wherever and whenever customers are seeking to buy. Third, they shorten the purchase funnel by demonstrating a product and describing why it is a must-have item; conveying that only limited quantities are available; counting down the time remaining on the item before the next item is to be introduced; and injecting such calls to action as "grab it before its gone." Finally, technology allows value capture in formats not possible previously: noncash payments (e.g., PayPal, Venmo), installment payments (e.g., Klarna unsecured loans), and barter payments (e.g., BarterOnly.com, which provides exchanges of used products). Marketers need to imagine how they can integrate such value creation and value capture opportunities made possible by technological advances.

Earlier studies used human intervention to collect data on emotional displays at a small scale (e.g., [ 8]; [30]; [42]). In contrast, applying artificial intelligence (see, e.g., [33]; [35]), we extract face presence and facial expressions from 62.32 million frames of streaming video sales presentations automatically and unobtrusively, thereby responding to calls to harness machine learning to generate meaning from big data (e.g., [ 4]; [29]; [40]; [59]).

Earlier studies consider either a single (e.g., [32]; [57]; [58]) or a select few emotions (e.g., [10]; [50]; [54]; [55]), potentially resulting in biased estimates due to omitted variables. Hence, Model 2 specifies a comprehensive set of six emotional displays simultaneously. Our focus on salespeople's emotional displays is also responsive to an earlier call to devote greater attention to emotions on the seller side of the exchange dyad (see [ 3]).

Earlier studies focus on static episodic expressions (e.g., [10]; [55]). Our Model 2 permits capturing the dynamic trajectories of emotions at a more granular level, thereby revealing time-varying patterns of sales impact (see Figure 3). More importantly, the Web Appendix makes original contributions to the theory of inference on the effects of functional principal components.

Virtually all studies on emotions have used customer mindset metrics as the dependent variables (e.g., [54]; [57]; [58]). While [32] use box-office revenues, they specify happiness to monotonically affect sales, ruling out the possibility of that an optimal level of emotions exists. Given the nonmonotonic effects in Models 3 and 4, the theoretical existence of the optimal mix arises. Equation 4 presents the optimal emotions to display so as to maximize sales. These results not only make original contributions to the extant literature but also offer guidance to design technology-inspired service agents (e.g., avatars, virtual news anchors) to be more humanlike ([11]; [37]). They also inform the discussion about technology and marketing in that artificial intelligence can be used to monitor the seller's facial activity, provide real-time coaching, and thus assist in training salespeople to improve business outcomes ([20]; [33]).

A marketplace increasingly characterized by greater technological connectivity and interactivity has prompted calls to investigate the business impact of a seller's facial expressions in screen-mediated commercial interactions ([ 7]). [28], for instance, underscore the need to evaluate whether their results from in-person, face-to-face customer encounters involving "emotionally calibrated" salespeople will hold in digital exchanges. The authors contend that this type of salesperson exhibits calmness and that exuding calmness builds rapport, which in turn drives favorable sales performance outcomes. Our theorizing, which is steeped in EASI's predictions about the inferences that buyers draw about a seller's facial expressions in a competitive exchange (see Table 1), and findings from a one-to-many livestream broadcast setting reaffirm the importance of reducing emotional displays in driving sales effectiveness. We thereby contribute to understanding the communicative role of facial expressions in screen-mediated exchanges and elaborate further in the following section on "selling with a straight face."

How should face presence be allocated over an item's presentation span? Should frames containing a face be displayed uniformly or in chunks? If the latter, should they be concentrated in the middle, when sales resistance is highest? To this end, we evaluate Equation 4 and present the optimal percentage allocation of the total number of frames with a face over an item's presentation span in Figure 9. We observe that the optimal allocation is neither uniformly displayed nor chunked in the middle. Rather, the optimal number of face frames wanes at the start, gradually builds to a crescendo, and eventually ebbs. Specifically, the optimal allocation decreases on the initial 10% span, then gradually increases as the presentation progresses, and finally decreases in the last 15% span. Remarkably, this optimal allocation conforms to the three-part structure of stories: the beginning, the middle, and the end (see [36]).

Figures 6 and 7 uncover the novel and provocative findings that, first, positive emotional displays reduce sales. Second, the greater the intensity, the larger the decline. To mitigate the negative effect, salespeople should consider toning down their facial expressions. To mitigate the quadratic effects of intensity, they can abate their exaggerated expressions. Together, these findings indicate a new maxim: sell with a straight face. Consistent with this maxim, [12], p. 82) advocates that direct marketers should use a "journalist approach" to answer the "who, what, why, when, where of a product. Whom is the product for? What does it do? Why is it beneficial? When can it be used? Where can it be bought?" In other words, livestream salespeople should broadcast their pitch with a stoic expression akin to that of news anchors, though we acknowledge that this implication may not generalize to face-to-face communications in business markets.

Figures 3 and 4 can be interpreted as the sales resistance curve. The maximum sales resistance is near the middle of an item's presentation; the least sales resistance is at the beginning and end of presentations. This U-shaped sales resistance curve provides actionable guidelines to practitioners. Emotional displays at the beginning and end of presentations help engage consumers and build rapport. However, during the livestream show, hosts should monitor the frequency and intensity of their emotional expressions. Because genuine interactions involve less emotional and more neutral expressions, salespeople can make emotional connections with the audience with neutral expressions and lessen the insidious effects of sales resistance. Although hosts cannot completely avoid emotions, they should take advantage of livestreaming platforms to emotionally connect the brands with customers.

A naïve assessment of a salesperson's performance is based on the actual quantity sold. However, this quantity depends on factors such as prices, duration, free shipping, and time of day and week. In other words, a salesperson can validly object that another salesperson's larger actual sales are not reflective of his/her performance alone because price, duration, free shipping, and time of day and week also impacted sales. Cognitive biases further compound such performance appraisals. Prominent factors driving cognitive biases include the fundamental attribution error, halo effects, the leniency bias, the recency bias, selective perception, the self-serving bias, and the similarity bias (e.g., [48]). Fundamental attribution error refers to supervisors underestimating the influence of external factors and overestimating the influence of internal factors when judging a salesperson's performance; halo effects arise when a general impression of a salesperson overshadows the relevant metrics; leniency bias refers to a supervisor's tendency to rate all salespeople positively (or negatively), reducing the difference between top and bottom performers; recency bias creeps in when recent events (e.g., bumper sales, sharp declines) influence supervisors' judgments; selective perception refers to the supervisor's tendency to notice certain metrics and filter out others; self-serving bias emerges when a salesperson attributes own successes to internal factors and failures to external factors; and similarity bias (i.e., homophily) shapes the evaluation when supervisors reward a salesperson similar to themselves.

Using Table 3, managers can objectively rank salespeople (see Panel C in Figure 5) to circumvent the effects of the aforementioned biases. Indeed, the CEO and senior leadership team of the livestream retailer we examined found our proposed framework valuable to recognize excellence and identify candidates for retraining. Both recognition and training, in turn, help improve future sales performance ([61]). Thus, the framework in Figure 1 unlocks the power of data and contributes to sales performance analytics.

[26]) recent study suggests that information content can matter. Specifically, they analyze 275 sales pitches from the Home Shopping Network, manually code the minute-by-minute content on the cumulative sales thus far during an item's presentation span, and show that the intermittent availability of this information increases item sales by.084 units at the onset and decreases linearly to.015 units at the end for a unit increase in the displayed cumulative sales. We encourage future researchers to automate such content analysis to extract and incorporate facial expressions.

Our empirical study pertains to one-to-many screen-mediated competitive exchanges, and it shows that the salesperson's emotional expressions evoke negative inferences by viewers about the salesperson's intentions. One explanation may be the absence of social interaction. When the salesperson smiles, the viewers may not reciprocate because the salesperson's emotions are not targeted to a specific viewer. Such differences provide the impetus to study screen-mediated face-to-face interactions in the presence of social others. For example, [21] contend that the customer purchase journey involves traveling with social others, which necessitates investigations into the various influences that members of the social network can have on buyers' appraisals, intentions, and actions. In a livestream e-commerce setting, the host becomes an important social other. Viewers can readily communicate with the influencer via live chat texts, emojis, voice, and/or video and further enhance their sense of connection with that celebrity (i.e., parasocial relationship). Does the host's verbal and nonverbal communications influence viewers' behavior in such communal, two-way screen-mediated exchanges? Do purchases by social others induce "fear of missing out"? [43] suggests conversion rates of 30% in livestream shopping versus 3% in traditional marketing. To incorporate such two-way communications in the models, researchers should augment the regressors with the characteristics of not only the items and sellers (as in this study), but also the network of social others and the hosts. We encourage further research to shed light on two-way communications in livestream shopping.

[25] manipulate authenticity (i.e., surface or deep acting) and emotional intensity in simulated service encounters (i.e., actors played the role of employees) with 223 consumers to understand the effects on customer satisfaction, customer–employee rapport, and loyalty intentions. They show that authenticity rather than intensity influences customers' reactions (for similar results, see [57]). We encourage future researchers to design emotion recognition algorithms that can classify facial expressions on the basis of emotional authenticity in addition to intensity.

Previous studies predominantly focus on marketing mix effects on sales because when they were conducted, machine learning technology was not available to detect faces and extract emotions at scale. This study combines machine learning technology and marketing. Specifically, we develop the retail analytics engine (see Figure 1) to unobtrusively collect data on face presence and emotional displays. Applying this technology to livestream retail data, we found that facial expressions, including happiness, adversely impact sales. This counterintuitive and provocative finding suggests that salespeople should sell with a straight face. These negative effects exhibit U-shaped dynamics over an item's presentation span, uniformly across six emotions, revealing that the largest sales resistance occurs during the middle of the presentation. Furthermore, the presence of a face matters because it impacts sales positively; therefore, it should be present more than is currently the case. Yet, its optimal allocation over time should be reduced over the initial 10% span, then gradually increased as the presentation progresses, and subsequently tapered down in the last 15% span. Finally, the retail analytics engine empowers managers to more objectively assess the effectiveness of each individual salesperson (see Figure 5), thereby circumventing cognitive biases in performance appraisals.

This study highlights the importance of monitoring and managing facial expressions. One implication is to train new salespeople. The firm can analyze the video footage, much like sports teams watch films of critical moments in previous games to learn what individual players did well and not so well, and sales coaches can help discern the extent to which they displayed emotions and the proportion of each emotion expressed. The feedback from such debriefing sessions could be used to modify sales pitches. Another implication is to retrain experienced sales professionals. The firm can compare each salesperson with the top performer (see Figure 5) and identify which emotions the salesperson ought to tackle. Happiness is the first one that should be addressed. While previous research advocates "service with a smile," we suggest selling with a straight face. Smiling may be off-putting because it lacks authenticity ([25]), reducing trust in the seller ([10]). Subsequently, salespeople should address displays of anger, then fear, and other negative emotions. Last, this study has implications for bot marketing. As technology advances, bots will more closely mimic human facial expressions and supersede humans in monitoring and managing facial expressions. Chat bots, like humans, provide voice assistance to customers. Similarly, three-dimensional audiovisual bots, like salespeople, can engage with customers. For example, HSBC Bank in Northern California employs Pepper, a social humanoid robot ([14]). Further technological advances will bestow bots with the ability to express and reciprocate emotions, thereby assisting livestream retailers to nudge prospective customers through the purchase funnel by explaining features and benefits, instilling urgency to buy, and entertaining them along the way.

sj-pdf-1-jmx-10.1177_00222429211013042 - Supplemental material for A New Livestream Retail Analytics Framework to Assess the Sales Impact of Emotional Displays

Supplemental material, sj-pdf-1-jmx-10.1177_00222429211013042 for A New Livestream Retail Analytics Framework to Assess the Sales Impact of Emotional Displays by Neeraj Bharadwaj, Michel Ballings, Prasad A. Naik, Miller Moore and Mustafa Murat Arat in Journal of Marketing

1 Michael Ahearne

2 The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

3 The author(s) received the following financial support for the research, authorship, and/or publication of this article: The first two authors received research support from the Haslam College of Business, and the third author acknowledges funding received from UC Davis research grants and the Class of 1989 Endowment.

4 Online supplement:https://doi.org/10.1177/00222429211013042

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~~~~~~~~

By Neeraj Bharadwaj; Michel Ballings; Prasad A. Naik; Miller Moore and Mustafa Murat Arat

Reported by Author; Author; Author; Author; Author

Acknowledgments 

Thank you to the following ad hoc reviewers who reviewed papers for the Journal of Marketing during 2021. The editors greatly appreciate their expert and constructive reviews.

Nidhi Agrawal, University of Washington

Joe Alba, University of Florida

Paulo Albuquerque, INSEAD

Rene Algesheimer, University of Zurich

B.J. Allen, Brigham Young University

Asim Ansari, Columbia University

Gil Appel, George Washington University

Deep Ashana, Duke University

Berk Ataman, Koç University

Ana Babić Rosario, University of Denver

Michelle Barnhart, Oregon State University

Yakov Bart, Northeastern University

Maren Becker, University of Cologne

Silvia Bellezza, Columbia University

Jonathan Berman, London Business School

Len Berry, Texas A&M University

Marco Bertini, ESADE Business School

Yashoda Bhagwat, Texas Christian University

Amit Bhattacharjee, INSEAD

Sean Blair, Georgetown University

Alexander Bleier, Frankfurt School of Finance and Management

Jeff Boichuk, University of Virginia

Raghu Bommaraju, Indian School of Business–Mohali

Samuel Bond, Georgia Institute of Technology

Andrea Bonezzi, New York University

Torsten Bornemann, Goethe University Frankfurt

Eric Boyd, University of Central Florida

Eric Bradlow, University of Pennsylvania

Josko Brakus, University of Leeds

Adam Brasel, Boston College

Els Breugelmans, KU Leuven

Aaron Brough, Utah State University

Alexander Brown, Texas A&M University

Tom Brown, Oklahoma State University

Melanie Brucks, Columbia University

Hernan Bruno, University of Cologne

Eva Buechel, University of Southern California

Jim Burroughs, University of Virginia

Romain Cadario, Erasmus University Rotterdam

Nuno Camacho, Erasmus University Rotterdam

Robin Canniford, University of Melbourne

Francois Carrillat, HEC Montréal

Steve Carson, University of Utah

Noah Castelo, University of Alberta

Lisa Cavanaugh, University of British Columbia

Deepa Chandrasekaran, University of Texas at San Antonio

Yubo Chen, Tsinghua University

Zoey Chen, University of Miami

Yimin Cheng, Monash University

Andrew Ching, Johns Hopkins University

Hana Choi, University of Rochester

Jeonghye Choi, Yonsei School of Business

Junhong Chu, NUS Business School

Luca Cian, University of Virginia

Danny Claro, Insper

Bart Claus, IESEG School of Management

Anatoli Colicev, Universita Bocconi

Yann Cornil, University of British Columbia

Gokcen Coskuner-Balli, Chapman University

Dave, Crockett, Moore School of Business

Tony Cui, University of Minnesota

Amy Dalton, Hong Kong University of Science and Technology

Derick Davis, University of Virginia

Arne De Keyser, EDHEC Business School

Bart de Langhe, ESADE

Yiting Deng, University College London

Berkeley Dietvorst, University of Chicago

Isaac Dinner, University of North Carolina

Beibei Dong, Lehigh University

Grant Donnelly, Ohio State University

Matilda Dorotic, BI Norwegian Business School

Rex Du, University of Texas at Austin

Shuili Du, University of New Hampshire

David Dubois, INSEAD

Adam Duhachek, University of Illinois at Chicago

Christine Eckert, University of Technology Sydney

Alexander Edeling, University of Cologne

Martin Eisend, European University Viadrina

Ryan Elder, Brigham Young University

Jenny Escalas, Vanderbilt University

Zachary Estes, City University of London

Jordan Etkin, Duke University

Theodoros Evgeniou, INSEAD

Peter Fader, University of Pennsylvania

Tingting Fan, The Chinese University of Hong Kong

Er Fang, Lehigh University

David Faro, London Business School

Philip Fernbach, University of Colorado at Boulder

Nathan Fong, Rutgers The State University of New Jersey

Bram Foubert, Maastricht University

Andrey Fradkin, Boston University

Rossella Gambetti, Università Cattolica del Sacro Cuore

Dinesh Gauri, University of Arkansas Fayetteville

Gary Gebhardt, HEC Montréal

Karen Gedenk, University of Hamburg

Katja Gelbrich, Catholic University Eichstätt-Ingolstadt

Sarah Gelper, Eindhoven University of Technology

Andrew Gershoff, University of Texas at Austin

Maggie Geuens, Ghent University

Mrinal Ghosh, University of Arizona

Michael Giebelhausen, Clemson University

Maarten Gijsenberg, University of Groningen

Ayelet Gneezy, University of California San Diego

Kelly Goldsmith, Vanderbilt University

Gabriel Gonzalez, San Diego State University

Shyam Gopinath, Indiana University

Dwayne Gremler, Bowling Green State University

Lauren Grewal, Dartmouth College

Rajdeep Grewal, University of North Carolina

Abhijit Guha, University of South Carolina

Kelley Gullo Wight, Indiana University

Tong Guo, Duke University

Aditya Gupta, Texas State University

Jonne Guyt, University of Amsterdam

Johannes Habel, University of Houston

Hanna Halaburda, New York University

Zachary Hall, Texas Christian University

Ryan Hamilton, Emory University

Kyuhong Han, Korea University

Mike Hanssens, University of California Los Angeles

David Hardisty, University of British Columbia

Jochen Hartmann, University of Hamburg

Gerald Häubl, University of Alberta

Mark Heitmann, University of Hamburg

Conor Henderson, University of Oregon

Dennis Herhausen, VU Amsterdam

Manuel Hermosilla, Johns Hopkins University

Christian Hildebrand, University of St. Gallen

JoAndrea Hoegg, University of British Columbia

Fei Fei Huang, Hong Kong Polytechnic University

Szu-Chi Huang, Stanford University

Christian Hughes, University of Notre Dame

Sam Hui, University of Houston

Tomas Hult, Michigan State University

Chris Hydock, California Polytechnic State University

Caglar Irmak, University of Miami

Masakazu Ishihara, New York University

Chris Janiszewski, University of Florida

Kamel Jedidi, Columbia University

He (Michael) Jia, University of Hong Kong

Niket Jindal, Indiana University Bloomington

Pranav Jindal, University of North Carolina at Chapel Hill

Leslie John, Harvard University

Eric Johnson, Columbia University

Jeff Johnson, University of Missouri-Kansas City

Mingyu Joo, University of California Riverside

Brett Josephson, George Mason University

Annamma Joy, University of British Columbia

Manish Kacker, McMaster University

Bernadette Kamleitner, Vienna University of Economics and Business

Diwas KC, Emory University

Kevin Keller, Dartmouth College

Kristopher Keller, University of North Carolina at Chapel Hill

Robert Kent, University of Delaware

Mansur Khamitov, Indiana University Bloomington

Sungjin Kim, University of Hawai'i at Manoa

TI Tongil Kim, University of Texas at Dallas

Anne Klesse, RSM Erasmus University

Ceren Kolsarici, Queen's University

Manfred Krafft, University of Münster

Raoul Kübler, University of Münster

V. Kumar, St. John's University

Ann-Kristin Kupfer, University of Muenster

Daniella Kupor, Boston University

Vardit Landsman, Erasmus University Rotterdam

Jan Landwehr, Goethe University Frankfurt

Andreas Lanz, HEC Paris

Kathryn LaTour, Cornell University

Justin Lawrence, Oklahoma State University Stillwater

Jeff Lee, American University

Ju-Yeon Lee, Iowa State University

Saerom Lee, University of Texas at San Antonio

Hongshuang (Alice) Li, Ohio State University

Jing Li, Nanjing University

Krista Li, Indiana University

Xi Li, University of Hong Kong

Yang Li, Cheung Kong Graduate School of Business

Yexin Li, University of Kansas

Noah Lim, National University of Singapore Business School

Hongju Liu, Peking University

Qiang Liu, Purdue University

Wendy Liu, University of California San Diego

Yan Liu, Texas A&M University

Jennifer Logg, Georgetown University

Chiara Longoni, Boston University

Carlos Lourenço, University of Lisbon

Mitch Lovett, University of Rochester

Michael Lowe, Georgia Institute of Technology

Tina Lowrey, HEC Paris

Shasha Lu, Fudan University

Andrea Luangrath, University of Iowa

Michael Luchs, College of William and Mary

Xueming Luo, Temple University

Nick Lurie, University of Connecticut

Richard Lutz, University of Florida

John Lynch, University of Colorado Boulder

Mike Lynn, Cornell University

Yu Ma, McGill University

Rhiannon, MacDonnell, University of Lethbridge

Andre Maciel, University of Nebraska–Lincoln

Shilpa Madan, Virginia Tech University

Durairaj Maheswaran, New York University

Ashwin Malshe, University of Texas at San Antonio

Alan Malter, University of Illinois at Chicago

Puneet Manchanda, University of Michigan

Murali Mantrala, University of Missouri

Greg Marshall, Rollins College

Charlotte Mason, University of Georgia

Marlys Mason, Oklahoma State University

Daniel McCarthy, Emory University

Ann McGill, University of Chicago

Ofer Mintz, University of Technology Sydney

Arul Mishra, University of Utah

Natalie Mizik, University of Washington

Daniel Mochon, Tulane University

Bhavya Mohan, University of San Francisco

Jakki Mohr, University of Montana

Erik Mooi, University of Melbourne

Jihwan Moon, University of New South Wales

Sangkil Moon, University of North Carolina at Charlotte

Sarah Moore, University of Alberta

Carey Morewedge, Boston University

Vishal Narayan, University of Connecticut

Sriram Narayanan, Michigan State University

Pravin Nath, Clemson University

Martin Natter, University of Zürich

Gideon Nave, University of Pennsylvania

Gergana Nenkov, Boston College

Nico Neumann, University of Melbourne

Sharon Ng, Nanyang Technological University

Hristina Nikolova, Boston College

Charles Noble, University of Tennessee Knoxville

Stephanie Noble, University of Tennessee Knoxville

Erica Okada, Hitotsubashi University

Jenny Olson, Indiana University Bloomington

Davide Orazi, Monash University

Yesim Orhun, University of Michigan

Ernst Osinga, Singapore Management University

Eunho Park, California State University Long Beach

Jeffrey Parker, University of Illinois at Chicago

Adithya Pattabhiramaiah, Georgia Institute of Technology

Joann Peck, University of Wisconsin-Madison

Andrew Perkins, Washington State University

Kirk Plangger, King's College London

Hilke Plassmann, INSEAD

Ruth Pogacar, University of Cincinnati

Evan Polman, University of Wisconsin–Madison

Linda Price, University of Wyoming

Davide Proserpio, University of Southern California

Andrea Prothero, University College Dublin

Marina Puzakova, Lehigh University

Yi Qian, University of British Columbia

William Rand, North Carolina State University

Anita Rao, University of Chicago

Vithala Rao, Cornell University

Thomas Reutterer, WU Vienna University

Daniel Ringel, University of North Carolina at Chapel Hill

Nicole Robitaille, Queen's University

Cristel Russel, Pepperdine University

Linda Salisbury, Boston College

Adriana Samper, Arizona State University

Gulen Sarial-Abi, Copenhagen Business School

Rom Schrift, Indiana University Bloomington

Janet Schwartz, Duke University

Sydney Scott, Washington University in Saint Louis

Seethu Seetharaman, Washington University in St. Louis

Kathleen Seiders, Boston College

Raj Sethuraman, Southern Methodist University

Julio Sevilla, University of Georgia

Franklin Shaddy, University of California Los Angeles

Avni Shah, University of Toronto Scarborough

Marissa Sharif, University of Pennsylvania

Amalesh Sharma, Texas A&M University

Eesha Sharma, San Diego State University

Edlira Shehu, Copenhagen Business School

Savannah Shi, Santa Clara University

Scott Shriver, University of Colorado at Boulder

Suzanne Shu, University of California Los Angeles

Sunil Singh, University of Nebraska–Lincoln

Vishal Singh, New York University

Robert Smith, Tilburg University

Jelena Spanjol, University of Munich

Stephen Spiller, University of California Los Angeles

Joydeep Srivastava, Temple University

Samuel Stäbler, Tilburg University

Rick Staelin, Duke University

Florian Stahl, University of Mannheim

Laurel Steinfield, Bentley University

Lena Steinhoff, University of Rostock

Jacob Suher, Portland State University

Harish Sujan, Tulane University

Yacheng, Sun, Tsinghua University

Sarang Sunder, Texas Christian University

Abby Sussman, University of Chicago

Courtney Szocs, Louisiana State University

Thales Teixeira, Decoupling.co

Artem Timoshenko, Northwestern University

Vilma Todri, Emory University

Gabriela Tonietto, Rutgers University

Rima Touré-Tillery, Northwestern University

Remi Trudel, Boston University

Yanping Tu, University of Florida

Anna Tuchman, Northwestern University

Catherine Tucker, Massachusetts Institute of Technology

Oleg Urminsky, University of Chicago

Demetrios Vakratsas, McGill University

Ana Valenzuela, Baruch College

Francesca Valsesia, University of Southern California

Bram Van den Bergh, Erasmus University Rotterdam

Ellis Van Den Hende, Delft University of Technology

Tom van Laer, University of Sydney

Noah Van Bergen, University of Cincinnati

Eric VanEpps, University of Utah

Rajan Varadarajan, Texas A&M University

Xavi Vidal-Berastain, Brandeis University

Scott Wallace, University of Washington

Melanie Wallendorf, University of Arizona

Echo Wan, University of Hong Kong

Jessie Wang, Miami University

Pengyuan Wang, University of Georgia

Yang Wang, Temple University

Morgan Ward, Emory University

Luc Wathieu, Georgetown University

George Watson, Portland State University

Klaus Wertenbroch, INSEAD

Hauke Wetzel, Massey University

Kim Whitler, University of Virginia

Jaap Wieringa, University of Groningen

Mike Wiles, Arizona State University

Russ Winer, New York University

David Woisetschläger, Technical University of Braunschweig

Kaitlin Woolley, Cornell University

David Wooten, University of Michigan

Stefan Worm, BI Norwegian Business School

Chunhua Wu, University of British Columbia

Freeman Wu, Vanderbilt University

Ying Xie, University of Texas Dallas

Alison Xu, University of Minnesota

Jennifer J. Xu, Bentley University

Adelle Yang, National University of Singapore

Nathan Yang, Cornell University

Yang Yang, University of Florida

Song Yao, Washington University in Saint Louis

Michael Yeomans, Harvard Business School

Gokhan Yildirim, Imperial College Business School

Sunyee Yoon, University at Buffalo

Tae Jung Yoon, Korea Advanced Institute of Science and Technology

Jonathan Zhang, Colorado State University

Jurui Zhang, University of Massachusetts-Boston

Kuangjie Zhang, Nanyang Technological University

Lingling Zhang, University of Maryland

Shunyuan Zhang, Harvard Business School

Ying Zhao, Hong Kong University of Science and Technology

Rongrong Zhou, Hong Kong University of Science and Technology

Meng Zhu, Johns Hopkins University

Ting Zhu, Purdue University

Peter Zubcsek, Tel Aviv University

An Emerging Theory of Avatar Marketing 

Avatars are becoming increasingly popular in contemporary marketing strategies, but their effectiveness for achieving performance outcomes (e.g., purchase likelihood) varies widely in practice. Related academic literature is fragmented, lacking both definitional consistency and conceptual clarity. This article makes three main contributions to avatar theory and managerial practice. First, to address ambiguity with respect to its definition, this study identifies and critically evaluates key conceptual elements of the term avatar, offers a definition derived from this analysis, and provides a typology of avatars' design elements. Second, the proposed 2 × 2 avatar taxonomy suggests that the alignment of an avatar's form realism and behavioral realism, across different contingencies, provides a parsimonious explanation for avatar effectiveness. Third, the authors develop an emerging theory of avatar marketing by triangulating insights from fundamental elements of avatars, a synthesis of extant research, and business practices. This framework integrates key theoretical insights, research propositions, and important managerial implications for this expanding area of marketing strategy. Lastly, the authors outline a research program to test the propositions and insights as well as advance future research.

Keywords: avatar; artificial intelligence; behavioral realism; chatbot; form realism; human–computer interaction

Advances in computer technology have supported the proliferation of virtual characters, broadly known as avatars, which we define as digital entities with anthropomorphic appearance, controlled by a human or software, that have an ability to interact. Companies are heavily investing in avatars to engage and serve their customers better, and the use of avatars is predicted to increase by 241% in the travel and hospitality industry and by 187% for consumer goods ([92]). In the banking industry, 87% of companies already use some form of an avatar or plan to implement one within two years ([93]).

Even as the use of avatars continues to rise, their effectiveness varies significantly across firms. For example, Progressive Insurance's avatar, Flo, serves many customers successfully on Facebook Messenger ([13]), but IKEA withdrew its avatar, Anna, from its online website following its unsatisfactory performance ([11]). No guidelines exist for effective design or use of avatars ([10]), and academic research often lags behind business practices. In addition, it is difficult to integrate extant research on avatars to establish a strong foundation because the literature in this domain is very fragmented and lacks definitional and conceptual precision, as is typical for an emerging research area. With this article, we aim to provide an integrated theoretical framework of avatars by establishing definitional and conceptual clarity, synthetizing academic research and business practices, and offering propositions that include both managerial insights and future research opportunities.

This article aims to make three main contributions to avatar theory and managerial practice. First, because extant research remains ambiguous with respect to defining and creating a taxonomy of avatars, it also remains difficult for researchers to compare empirical results and draw meaningful conclusions across studies. We offer an overview, in which we summarize the various ways the term avatar has been defined, identify and critically evaluate key conceptual elements of these definitions, and propose a definition on this basis. Then, drawing on this definition, we present a typology of avatar design to isolate elements that academics and managers can leverage to ensure avatars' effectiveness for achieving specific goals (e.g., providing standard vs. personalized solutions). This typology represents an overall organizing framework for thinking about the uses of avatars, making relevant design/implementation decisions, and identifying research gaps.

Second, applying our proposed avatar typology, we synthesize findings from prior literature and business practices to produce a 2 × 2 taxonomy composed of two dimensions: avatars' form realism and behavioral realism. This taxonomy enables us to generate specific research propositions to advance our understanding of and improve effectiveness of avatars in marketing. The level of alignment between an avatar's form and behavioral realism across different contingencies provides a parsimonious explanation for when an avatar is most effective, which we highlight with examples of successful and failed avatars in business practices. In particular, avatars with high form realism but low behavioral realism, which we call superficial avatars, can hinder customer experiences in high-risk transactions (e.g., stock purchases) because the misalignment between an anthropomorphic appearance and low intelligence leads to negative disconfirmations of expectations (e.g., Nordnet's Amelia exhibits realistic human appearance but does not offer good stock tips). In contrast, an intelligent unrealistic avatar (i.e., low form realism, high behavioral realism) may produce positive disconfirmations in socially complex interactions that require exchanges of sensitive personal information (e.g., Ellie, an online avatar, helps assess people's depression and posttraumatic stress disorder [PTSD] symptoms); because the avatar's unrealistic human appearance cannot be mistaken for a real human, people are more likely to provide responses that are free from a social desirability bias.

Third, by triangulating insights from the avatar fundamentals and our synthesis of the extant research and business practices, we develop an integrative framework of avatar performance and offer key theoretical insights, research propositions, and managerial implications for this expanding area of marketing strategy. This framework sheds new light on the underlying mechanisms of effective avatar design and implementation strategies, as well as potential contingencies to these effects. In turn, we offer managerial guidelines regarding avatar deployment, specific decision rules, and insights into how and why specific avatar strategies may be effective. This foundation for a contemporary theory of avatars may spur future research; the identified academic–practice gaps and propositions in particular point to promising research directions.

The popularity of avatars is fueled by two macroenvironmental factors. First, advancements in computer/digital technologies (e.g., artificial intelligence [AI]) enabled the development of more complex avatars, and they often appear in three-dimensional (3D) forms, imbued with seemingly distinctive personalities, appearances, and behavioral patterns, and are overall more appealing than the previous, simple versions ([ 3]; [40]). Second, increase in the use of avatars reflects the growing importance of online service experiences, such as education, gaming, banking, and shopping ([40]; [56]), which firms want to make as convenient and hassle-free for customers as possible ([58]). For example, online customers often express frustration when they cannot find relevant information on a website quickly and easily; avatars can effectively and efficiently provide a solution to this issue. Today's customers also expect faster communication from companies, but immediate responses tend to be difficult and expensive for firms to achieve through traditional channels (e.g., face-to-face, telephone) ([54]). Shopping in the online environment also reduces customers' sense of social interaction and personal consultation, a concern that avatars can help address ([45]). Finally, recent technology developments enable avatars to offer expanded benefits to firms, in that "avatars capable of having complex and interactive conversations with customers will exponentially increase the amount of data businesses can access. Avatars can potentially know if [customers] were bored or happy in real time and know the exact moment someone became disengaged" ([37]).

Although we can readily list the benefits of avatars, no strong consensus exists regarding their precise definition (Table 1). Furthermore, academics have used multiple terms interchangeably to refer to avatars, such as automated shopping assistants ([ 4]), chatbots ([44]), virtual customer service agents ([96]), embodied conversational agents ([ 7]; [59]; [86]), or virtual/digital assistants ([18]; [38]). The ambiguity surrounding the definition of avatars makes it difficult for researchers to compare empirical results or draw meaningful conclusions across studies ([74]). To advance scientific knowledge, we need a precise definition that clearly delineates the boundaries of the construct. In this section, we review various ways avatars have been defined, identify and critically evaluate some key definitional elements, and offer a new definition derived from this analysis.

Graph

Table 1. Avatar Definitional Elements in Empirical Research.

One aspect on which there is no consensus when it comes to defining avatars is whether avatars need to have an anthropomorphic appearance ([74]). Anthropomorphism refers to "the extent to which an image looks human" ([72], p. 154). In prior academic research, 70% of articles identify an anthropomorphic or humanlike appearance as a necessary condition of a conceptual definition of an avatar. This element is important because the degree to which an avatar is anthropomorphic provides cues of its social presence ([69]). Research shows that the more anthropomorphic an avatar is perceived to be, the more credible and competent it seems ([99]), such that "a person may be represented by a highly accurate and lifelike avatar of a fir tree. Although this avatar is realistic, other users may be less likely to attribute social potential to it—and less likely to communicate with it—because it is not anthropomorphic" ([74], p. 37). Research shows that how anthropomorphic we perceive something to be impacts our expectations of certain behaviors and our willingness to interact; people treat something with a human appearance differently than they do inanimate objects ([36]). For example, Neytiri in the film Avatar is not a human, but because she has an anthropomorphic appearance, other characters interact with her the same way they would with a human.

Knowledge about how to deal with other humans generally is learned early in life and is more detailed and readily accessible in people's memory than knowledge about how to interact with inanimate objects ([30]). According to the computers-as-social-actors (CASA) paradigm ([68]; [70]; [84]), people tend to treat computer technology that exhibits humanlike characteristics as a social actor and apply the same social rules to it during interactions, despite being fully aware that they are dealing with a machine ([45]). The presence of an anthropomorphic appearance triggers people's simplistic social scripts (e.g., politeness, reciprocity), which in turn induce cognitive, affective, and social responses during interactions with technology ([98]). Thus, we regard an anthropomorphic appearance as an important, required element of the conceptual definition of digital avatars, because people interact differently with something they perceive as more "human." This requirement, thus, would exclude inanimate objects and brands as well as voice-only digital assistants that lack an anthropomorphic appearance.

Interactivity refers to "the extent to which individuals perceive that the communication allows them to feel in control as if they can communicate synchronously and reciprocally with the communicator" ([18], p. 317). In defining interactivity as another critical requirement for digital avatars, we refer specifically to the ability to engage in two-way interactions, which may be verbal (voice) or nonverbal (text, animation). Prior research has established three dimensions of interactivity: ( 1) the user's active control, or ability to participate and influence communication; ( 2) bilateral interactions; and ( 3) synchronicity ([32]; [61]). Approximately 78% of the research we reviewed include interactivity as one of the elements of the conceptual definition of avatars. For example, in defining avatars as "virtual characters that can be used as company representatives in online stores," [60], p. 2) allow them to be noninteractive and capable of only one-way conversation, such as welcoming users, introducing the company, or describing available products. Some other researchers similarly do not consider interactivity as a necessary element of digital avatars (e.g., [48]). Yet most researchers focus on interactive avatars and find that they can increase customers' satisfaction with a website or product, credibility, or patronage intentions ([17]; [45]).

However, designing a truly interactive avatar that can engage in synchronous communication is not an easy task: "Natural language dialogue in chatbots suggests a low threshold for users to access data and services. However, whereas conversational interfaces are truly intuitive when applied to interactions between people, conversations between humans and automated conversational agents are more challenging" ([11], p. 41). The interactivity requirement would exclude entities such as "self-avatars" in clothing stores, which do not offer bidirectional communication, as well as any instances of asynchronous content, such as a lecture delivered as a prerecorded video of the instructor, or a standard greeting from a chatbot that cannot offer personalized interactions with each user. However, when there is true bidirectional interactivity, it can satisfy customers' hedonic (e.g., having fun while shopping on a website) and utilitarian (e.g., efficiently finding a solution to a problem on a website) needs ([60]). Thus, we include it as a requirement for digital avatars.

Researchers also have different perspectives on the controlling entity, which refers to whether the control over an avatar involves a human operator or an automated computer program ([74]). Some researchers make this distinction explicit and refer to anything controlled by technology as an agent or bot, while referring to anything controlled by humans as an avatar ([74]). However, in business practices, due to cost considerations, digital avatars appear almost exclusively enabled by AI (e.g., Sophie, the Air New Zealand customer service rep). Yet we have no theoretical reason to limit the conceptual definition of avatars to only those which are enabled by AI, because it seems that consumers want a perception of an avatar having some level of intelligence but often cannot tell precisely who or what controls it ([57]), as there are typically no solid clues available. According to the modality–agency–interactivity–navigability model and its applications in virtual environments, if agency cues are present in an interface, they influence users' perceptions by prompting their cognitive heuristics about the nature and content of the interaction ([91]). Users' perceptions and behaviors thus differ if they learn that they are interacting with an AI-backed avatar versus one controlled by a person, reflecting the different heuristics that are evoked by machine versus human counterparts ([36]; [42]). Thus, "identity cues suggesting that the user is chatting with a human agent or machine agent can trigger human or machine heuristics respectively and accordingly affect the criteria by which they evaluate the quality of the interaction" ([42], p. 305).

In summary, we define avatars as digital entities with anthropomorphic appearance, controlled by a human or software, that are able to interact. Among the academic papers we reviewed, about half (51%) include all of these elements in their conceptual definitions of avatars; the inclusion rates for each specific definitional element vary between 70% and 90%.

Drawing on this derived definition of avatars, we propose a typology of avatar design. This typology allows academics and managers to isolate elements that make an avatar more or less effective for specific goals, such as providing product information, answering customers' process questions, and so forth. Furthermore, this typology provides an overall organizing framework for thinking about, making design/implementation decisions regarding, and researching avatars.

Different design elements cause avatars to vary in their visual appearances and behaviors during interactions with humans. All of the design elements affect avatars' form realism and behavioral realism. Form realism refers to the extent to which the avatar's shape appears human, while behavioral realism captures the degree to which it behaves as a human would in the physical world ([ 5]; [ 9]; [36]). Some researchers argue that behavioral realism is more important than form realism ([ 9]), but both form and behavioral realism are associated with greater avatar usefulness in most contexts ([39]; [51]; [104]). Figure 1 provides an overview of all the design elements, with examples, that managers can use to understand and influence the degree of avatars' form and behavioral realism.

Graph: Figure 1. Typology of avatar design.aAlthough these specific avatar examples are used to illustrate a specific characteristic, they also satisfy all of the other requirements of our conceptual definition of avatars.b[15], p. 9.Notes: For live links to the URLs noted herein, see the Supplemental Material link.

Higher form realism may lead users to develop social expectations for their subsequent interactions with avatars ([73]). Managers can impact the degree of form realism of an avatar through the design elements such as spatial dimensions (2D vs. 3D avatars), ability to have movement in the face or body (visually static vs. dynamic avatars), and other characteristics that enhance the perception of "humanness" of avatars, such as signals of gender, race, age, or names.

Avatars can be 2D or 3D. In the sample of articles we reviewed, 52% focused on 2D avatars and 48% on 3D avatars. Research indicates that 3D avatars are perceived as more compelling and impactful, relative to 2D versions ([ 5]; [36]; [81]).

Both technological advancements and customer expectations have prompted the development of more realistic, visually dynamic avatars that can move their bodies and faces ([106]). In our sample of reviewed studies, 38% of studies examined static avatars, while 62% considered visually dynamic avatars. For example, Amelia is a visually dynamic avatar that is capable of facial expressions and movement and has been used, with modifications, in various industries such as banking, insurance, and health care ([46]). Visually dynamic avatars with ability for facial expressions can convey emotions, which is especially helpful for customers from different cultural backgrounds: "avatars with high intensity expression and dynamics allow both the local and global audiences to achieve approximately equal levels in subject identification and emotion perception" ([106], p. 21). Thus, visually dynamic avatars may be more effective for global corporations. Additional evidence indicates that the greater an avatar's ability to exhibit facial expressions, the less perceived human agency is required to exert social influence ([ 5]).

To enhance form realism, avatars can be designed to include additional "human" elements, such as an identifiable name, gender, race, and age. In the studies we reviewed, out of the aforementioned set of characteristics, gender and age are the most commonly studied ones, followed by name and race. For example, the Air New Zealand virtual customer service avatar is female, named Sophie. Research shows that characteristics such as gender can increase the effectiveness of avatars ([71]).

Avatars' behavioral realism can facilitate more natural interactions with users ([ 9]), and managers can manipulate the degree of avatars' behavioral realism using design elements associated with avatars' interactivity and controlling entity. Specifically, relevant design elements that managers can use to impact avatar interactivity are communication modality (avatars' ability to communicate verbally, nonverbally, or through a combination of both), response type (whether avatars' responses are scripted or natural), and presence of social content (whether avatars can engage in interactions about social and personal matters in addition to task-oriented communications). In terms of controlling entity, avatars can be controlled by a computer program or algorithm or a human, with the latter, predictably, increasing avatars' behavioral realism.

Avatars differ in the modalities of communication they use. Nonverbal avatar communication can be represented by text (speech-to-text avatars), gestures, or facial expressions; verbal avatars communicate via speech; and both nonverbal and verbal avatars can communicate using a combination of these modalities. The latter category would be the highest in behavioral realism from a communication modality perspective. Research investigating the communication modality of avatars has focused primarily on avatars that are capable of both verbal and nonverbal interactions, accounting for 68% of reviewed articles, followed by research on non-verbal avatars in 26% of articles, with verbal avatars attracting the least academic attention (6%).

In addition, managers might increase behavioral realism by enabling avatars to recognize the nonverbal behaviors of users, such as their facial expressions, prompting more appropriate responses. For example, Microsoft's XiaoIce can interpret users' photos and make relevant inferences and comments ([28]). However, even with significant advances in AI, creating an avatar capable of correctly identifying and responding to users' various emotions and contexts remains a challenge because "large, interpersonal variability exists in how people express emotions. Humans also have diverse preferences for how an agent [avatar] responds to them" ([64], p. 76).

Managers can design avatars with an ability to converse in a way that feels natural to users. Previous research has mostly (60% of articles) focused on avatars that are capable of merely selecting a response from a set of preexisting, predetermined, scripted responses. For example, HSBC's virtual assistant, Amy, currently is able to select and provide users with only predetermined responses about a limited number of the bank's products. Avatars with a capability for natural responses instead can have a "relatively free-flowing conversation, using accepted vocabulary and grammar, and with the ability to track the context of the conversation and make appropriate responses" ([15], p. 9). For example, the skincare company SK-II's YUMI understands users expressing themselves in their own words and responds in an organic, conversational manner. The ability to have a conversation that feels natural is also highly correlated with perceived agency type; avatars controlled by humans would have a natural response, whereas software-controlled avatars tend to rely on scripted responses.

Another design element that can increase avatars' interactivity is their ability to provide some social content during interactions with users, as opposed to purely task-oriented communication (e.g., providing product information). For example, Microsoft's XiaoIce is an AI assistant that also attempts to function like a friend, checking on users after a relationship breakup or asking about the physical recovery of a user who posted a photo of a bruised leg. Since its launch in China in 2014, XiaoIce has gained great popularity, due to its emotional intelligence: "The real key takeaway is that we've focused on emotional intelligence.... We call this an empathetic computing framework, [designed to] have conversations with humans naturally, which can build a social and emotional connection. It's a good friend. As a result, they can better participate and help out in human society" ([28]). In the research we reviewed, 34% of avatars could offer some social content during their interactions.

Research shows that consumers interacting with an avatar that they perceive to be controlled by a human behave differently from customers who believe the avatar is controlled by software. According to a meta-analysis, avatars controlled by humans elicit more presence and a stronger social influence than do computer-controlled avatars ([36]). Therefore, for firms that rely on software-controlled avatars (the majority of avatars in practice), reinforcing "human" elements can be very effective. They can leverage the design elements we have discussed, such as more natural speech programmed for software-controlled avatars, non-verbal behaviors and emotions, and the ability to provide social content. However, perceptions of human agency might not be desirable in all settings, as research shows that people perform worse on certain tasks when they recognize that they are interacting with a human-controlled avatar rather than software, due to social inhibition, social desirability bias, and perceptions of reduced autonomy ([56]; [105]). For example, avatars have become popular in health care, and when it comes to disclosing sensitive information such as drinking habits, users are more comfortable revealing information if they perceive less human agency ([86]).

In the proposed typology, all of the design elements serve to increase or decrease the form and behavioral realism of avatars. Most prior research investigates only a narrow set of design elements in piecemeal fashion, independent of other elements, or at an aggregate level, without considering the granularity or interactions of specific elements. In the following sections, we synthesize academic literature and business practices related to avatars to provide insights and identify research gaps. We also derive propositions to help advance theory, inform business practices, and guide future research.

Because research on avatars is diverse and cross-disciplinary in nature, our goal is to provide a representative rather than exhaustive literature review, covering a variety of research disciplines and empirical settings. Using "avatar" and related terms[ 5] as keywords, we searched article titles and abstracts in electronic databases (Academic Search Complete, Business Source Complete, Science Direct, and Google Scholar) to find studies published during the 1990–2020 period. To identify research involving avatars that are consistent with our definition, we first excluded articles that did not provide detailed information about avatar definitional elements or scope. In addition, we excluded research on topics that fall outside our definitional boundary (e.g., consumers' self-avatars). Purely technical articles (e.g., programming of avatar) and studies that do not address real-time consumer–avatar interactions were removed as well. To strike a good balance between research diversity and quality, we sampled only reputable journals across various disciplines (i.e., impact factor of at least 3).[ 6] In total, we compiled 98 empirical research articles (the full list of reviewed research is available in the Web Appendix). Table 2 provides a summary of select illustrative research.

Graph

Table 2. Avatars in Empirical Research.

The overarching theoretical framework that guides empirical studies of human–avatar interactions is social response theory, which is sometimes referred to as the CASA paradigm ([68]; [70]; [84]). It suggests that anthropomorphic characteristics of avatars elicit consumers' socialness perceptions, often in an automatic, spontaneous, mindless process that induces varying degrees of cognitive, affective, and social responses to avatars ([ 4]; [45]; [96]; [98]). Although the theory suggests that an anthropomorphic appearance positively affects customer outcomes, empirical results indicate some mixed effects. In various situations, lower or higher levels of form realism appeared more effective, but in other cases, no differences emerged. For example, visually static, cartoonish avatars with very low form realism increased satisfaction with a retailer, attitude toward products, and purchase intentions in some studies ([31]; [45]). However, [83] find that avatars with more realistic, humanlike appearance increased customers' perceptions of social presence, leading to higher usage intentions. [96] found no significant differences between avatars that are low or high in form realism in terms of service satisfaction. Similarly, [86] reported that a more anthropomorphic appearance of an avatar had no effect on participants' disclosure of information about sensitive topics, such as drinking behaviors.

Two factors may help explain these inconsistent effects. First, prior studies have not investigated all of the design elements identified in our typology that help establish avatars' form realism (e.g., visually static vs. dynamic avatars, avatars' age, gender) or how these underlying elements might induce specific effects. By focusing on only a subset of visual characteristics and, thus, failing to account for the totality of the elements that establish form realism of an avatar, these studies may have produced biased estimates. Second, we propose that avatars' form and behavior must be considered simultaneously, because form realism is meaningful only in the context of behavioral realism ([ 5]); however, few studies have done so.

Extant research consistently shows positive effects of greater behavioral realism. For example, [98] report that an avatar's scripted text or spoken communications can enhance customers' hedonic and utilitarian benefits when shopping online, as well as increasing their patronage intentions. Similarly, [59] find that when users interact with an avatar that has a high degree of behavioral realism, trust between the parties is higher. Other studies offer similar results, noting that avatars' behaviors, such as decision-making style ([ 4]) and socially oriented communication ([96]), significantly affect avatar trustworthiness and the overall customer experience ([12]; [18]).

Advancements in AI technology have enabled avatars to exhibit higher levels of cognitive and emotional intelligence. For example, they can engage in autonomous conversations by analyzing and responding to customers' requests in real time, thereby significantly increasing customers' trust in them ([64]). Using avatar interviewers equipped with video, audio sensors, and advanced analytical software, [75] demonstrate how intelligent avatars can detect, interpret, and respond to human interviewees' emotions, cognitive effort, and potential deceptions. [97] examine the effects of an avatar that can collect and analyze a human's voice and upper-body movement and coordinate its own responses accordingly. Results show that the avatar's intelligent behavior led to positive evaluations of the avatar, regardless of whether it was controlled by a human or a software.

Even in light of these consistent findings related to avatar behavioral realism, some important research issues remain unresolved. First, few studies have investigated the underlying behavioral realism elements identified in our typology of avatar design (e.g., communication modality, social content, response type) to determine which are most critical or how they might interact with other form or behavioral realism elements. For example, [ 7] found that an avatar nurse incorporating social content in its scripted conversations produced better patient experiences, but [86] reported that a scripted, task-focused avatar interviewer elicits more socially biased responses. Second, a few studies revealed some unexpected negative effects of behavioral realism (e.g., [86]), but research has yet to identify the conditions in which detrimental effects are more likely or design strategies to address them.

Our review of extant literature thus reveals a key limitation: lack of consideration of the alignment between form and behavioral realism of avatars. If the levels of form and behavioral realism are mismatched, the consequences for avatars' effectiveness may be profound and can help explain inconsistent past findings. Yet some misaligned avatars (e.g., the REA avatar is high in behavioral realism but low in form realism) seem equally as effective as well-aligned avatars (e.g., SK-II's skincare advisor YUMI is very high in both behavioral and form realism). However, other misaligned avatars have failed (e.g., Nordnet's Amelia, with high form realism but low behavioral realism). A systematic analysis of avatar effectiveness thus seems warranted and requires identifying different categories of avatars along the form and behavioral realism dimensions as a first step.

We suggest that avatars can be parsimoniously grouped into a 2 × 2 taxonomy, according to their form and behavioral realism (Figure 2). This taxonomy provides a foundation for predicting the success or failure of avatars in business practices and can inform avatar design strategies. We identify four distinct categories of avatars: simplistic, superficial, intelligent unrealistic, and digital human avatars. A simplistic avatar has an unrealistic human appearance (e.g., 2D, visually static, cartoonish image) and engages in low intelligence behaviors (e.g., scripted, only task-specific communication). For example, in the Netherlands, ING Bank uses a 2D, cartoonish-looking avatar, Inge, to provide responses to simple customer inquiries with a set of predetermined answers. In contrast, a superficial avatar has a realistic anthropomorphic appearance (e.g., 3D, visually dynamic, photorealistic image), such as Natwest Bank's Cora, but low behavioral realism, in that it is only able to offer preprogrammed answers to specific questions. An intelligent unrealistic avatar (e.g., REA) is characterized by humanlike cognitive and emotional intelligence but exhibits an unrealistic (e.g., cartoonish) human image. These avatars can engage customers in real-time, complex transactions without being mistaken for real human agents. Finally, a digital human avatar such as SK-II's YUMI is the most advanced category of avatars, characterized by both a highly realistic anthropomorphic appearance and humanlike cognitive and emotional intelligence, and is designed to provide the highest degree of realism during interactions with human users.

Graph: Figure 2. Form realism versus behavioral realism taxonomy.

To advance extant literature, we propose the need to consider the interrelationship of form and behavioral realism. Visual information (i.e., what an avatar looks like) often gets processed automatically and almost immediately, requiring minimum cognitive resources ([65]). This visual appearance then becomes the basis for probabilistic consistency inferences, where consumers form an expectation of some unknown attribute, based on a known attribute with which it is believed to be correlated ([27]). For example, consumers often make inferences about an unfamiliar brand's quality by using price as a signal of quality, in the belief that the two are correlated. Similarly, when an avatar looks more like a human, consumers may expect it to also behave like a human. Thus, the visual characteristics of an avatar may influence consumers' judgments of its behavioral competence, even before an interaction takes place ([73]). A more realistic anthropomorphic appearance suggests a higher level of behavioral realism, leading to a greater expectation that the avatar will behave like a real human might.

• Proposition 1: As the form realism of an avatar increases, so do customers' expectations for its behavioral realism.

Customers will use an avatar's form realism as a frame of reference for forming initial expectations about its behavioral realism. The expected level of behavioral realism will then serve as a benchmark, against which consumers will form comparative judgments of their subsequent experience. According to expectation confirmation theory ([76]), when the actual outcome is worse than expected, consumers experience a negative disconfirmation, leading to decreased overall satisfaction. A better-than-expected outcome instead results in a positive disconfirmation, which increases customers' overall satisfaction ([33]; [100]).

Consistent with this theory, if an avatar's behavioral realism exceeds the consumer's initial expectations, which were based on the avatar's form realism, a positive disconfirmation likely occurs, and the consumer should perceive the avatar as more credible and attractive, as well as feel increased trust or confidence in it ([ 2]). Ellie, an avatar that assesses depression and PTSD symptoms in veterans, serves as a good illustration of a positive disconfirmation: her cartoonish appearance paired with highly intelligent, humanlike behavior has proven highly effective with vulnerable individuals ([85]). Conversely, if the avatar's behavioral competency falls short of the expectations that users formed on the basis of the avatar's form realism, it may lead to a negative disconfirmation and dampen customers' satisfaction ([65]). Nordnet's Amelia exhibited minimal competence in providing customized advice for high-risk transactions (e.g., stock purchase), which proved disappointing. Amelia's realistic anthropomorphic appearance might have led customers to develop high behavioral expectations, which Amelia could not deliver, giving rise to a negative disconfirmation. Therefore, we expect asymmetric effects of misaligned avatar form and behavioral realism.

• Proposition 2: Differences between the avatar's form and behavioral realism have asymmetric effects, such that customers experience positive (negative) disconfirmation when an avatar's behavioral realism is greater (less) than its form realism.

Integration of evidence across multiple research streams suggests that avatars affect performance outcomes (e.g., customers' likelihood to purchase a product) indirectly through customers' cognitive, affective, and social responses, depending on the context ([45]; [59]). Customers form cognitive responses to avatars according to the avatars' informativeness or competence in helping them make well-informed decisions ([45]; [98]). Cognitive trust, or willingness to rely on another entity's help to achieve goals in uncertain situations, is another key dimension of customers' cognitive response ([53]; [63]).

Interactions with avatars can also evoke affective responses in customers, such as by providing them with unique entertainment experiences ([45]). Avatars can deliver pleasurable experiences independent of their ability to facilitate a specific functional goal, such as a shopping task, by offering entertainment and emotional value during the shopping process ([98]). Human–avatar interactions are also social in nature. Avatars can enhance customers' perceived social presence (i.e., the feeling of being with another person) and create feelings of human contact or connection ([18]; [83]). Moreover, the use of an avatar can provide a sense of personalization, so customers receive information that appears tailor-made to their specific needs ([96]; [98]). The CASA framework argues that avatars can also induce feelings of reciprocity in human–computer interactions, which can strengthen perceived rapport with the avatar and enhance the users' social experience ([18]; [59]).

When the levels of an avatar's form realism and behavioral realism are aligned, customers' behavioral expectations tend to be confirmed. This simple confirmation, together with high initial behavioral expectations induced by form realism, may have a strong, positive, additive effect on performance outcomes, such as customers' purchase likelihood ([76]; [90]). We expect that avatars that are aligned in their form and behavioral realism can affect customer performance outcomes through all three types of mediating responses: cognitive, affective, and social. However, when the levels of form and behavioral realism are misaligned, the outcomes might be mediated through different responses. Consider the misalignment that occurs when form realism exceeds behavioral realism. In this situation, customers may find the avatar to be especially entertaining, because its realistic anthropomorphic appearance and characteristics can also serve as hedonic elements that can increase perceived entertainment, which often is intrinsically enjoyable in its own right, regardless of performance outcomes ([24]; [103]). To the extent that perceived enjoyment creates a pleasant mood, the hedonic aspect of high form realism can improve performance outcomes such as impulsive online purchases ([78]). Yet customers also might experience negative disconfirmation, stemming from the disappointment with an avatar's cognitive and social capabilities, resulting in weakened customer performance outcomes.

Alternatively, when misalignment arises because the avatar's behavioral realism exceeds its form realism, the positive effects on customer outcomes might be mediated primarily by cognitive and social responses. Although typically this avatar's lower form realism is unlikely to provide much entertainment for the customer,[ 7] the positive disconfirmation of avatar's behavioral competence may significantly boost customers' confidence in the avatar's overall ability to provide valid information, offer personalized service, or build customer rapport.

• Proposition 3: When an avatar's form realism exceeds its behavioral realism, it has a positive effect on performance outcomes (e.g., purchase likelihood), through customers' (a) affective responses, but a negative effect on performance outcomes through customers' (b) cognitive and (c) social responses.
• Proposition 4: When an avatar's behavioral realism exceeds its form realism, it has a positive effect on performance outcomes (e.g., purchase likelihood) through customers' (a) cognitive and (b) social responses.

As we noted previously, many companies are adopting avatars to humanize their brands with a scalable personalized human touch, but managers lack guidance for how to design these avatars to ensure their effectiveness ([10]; [101]). In this section, we use business examples to illustrate the avatar categories from our taxonomy (Figure 2) and thereby clarify which factors and conditions make them effective. We also generate theoretical insights and managerial implications.

An obvious benefit of using avatars is the firm's improved efficiency and scalable customer service. For example, ING Bank's cartoonish avatar, Marie, answers common debit and credit card questions with preprogrammed information and solutions (www.ing.com). In the Los Angeles Superior Court, an animated cartoon avatar, Gina, which speaks multiple languages, successfully handles 1.2 million new traffic citations a year ([62]). A start-up company called TwentyBN has introduced an animated cartoon sales avatar, Millie, which can understand and answer simple questions while presenting various products ([49]) and seems to be especially effective in promoting low-ticket items, such as sunglasses. Simplistic avatars thus seem most effective in providing hassle-free, convenient options for completing quick, specific tasks (e.g., information inquiries), especially when relatively little risk is involved (e.g., inexpensive online purchases).

The use of superficial avatars in various industries shows more mixed results. Among the successes, HSBC Hong Kong's Amy, a photorealistic avatar that handles routine customer inquiries similar to ING's Marie, was well-received by customers ([93]). A very realistic-looking 3D avatar, Cora of Natwest Bank in the United Kingdom, can answer 200 basic questions, such as how to open an account or complete a mortgage application ([79]). In the insurance industry, Lemonade Insurance's avatar Maya and Progressive's Flo, both very humanlike, are programmed to provide category-specific information and handle simple transactions, such as onboarding customers and giving online quotes ([13]; [82]). However, other superficial avatars have been less effective. The Swedish bank Nordnet had to discontinue its realistic-looking avatar Amelia, presumably due to her failure to provide intelligent stock purchasing advice. At IKEA, the decision to eliminate its avatar Anna stemmed from a recognition that her realistic anthropomorphic appearance led to complex customer questions, which required responses beyond the predetermined set available in the avatar's programming ([11]; [87]). Overall, superficial avatars can entertain customers while enhancing efficiency in low-risk transactions (e.g., bank account information inquiries), but they also can produce detrimental effects for customers seeking high-risk or complex transactions (e.g., financial investments) because these avatars lack the level of intelligence that their realistic anthropomorphic appearance leads users to expect.

This type of avatar is relatively rare but generally successful. For example, the REA avatar has been effective in providing virtual showings of homes for sale; Ellie, an avatar therapist, has been useful in detecting PTSD and depression symptoms in military veterans. With its humanlike intelligence, Ellie can engage in context-appropriate, autonomous conversations and build rapport with subtle, supportive, and sympathetic gestures when listening to a veteran's sensitive story. In turn, veterans disclose significantly more PTSD symptoms to her than to a human therapist ([43]). Thus, intelligent unrealistic avatars seem especially effective for complex relational transactions involving sensitive personal information (e.g., finances, health) as they can provide a sense of nonjudgment because customers recognize that these avatars are not human but are still competent in their tasks.

With advanced digital and computing technologies, pioneer avatar companies such as Soul Machines are breaking new ground for digital human avatars in marketing applications (www.soulmachines.com). For example, skincare brand SK-II uses an incredibly realistic looking and behaving avatar, YUMI, whose AI-powered digital brain enables advanced cognitive and emotional intelligence. In addition, YUMI can recognize users' gestures and features, such as eye color; communicate via speech or text; and deliver customized tips with credible, highly personalized beauty advice ([14]). Another digital human avatar, modeled after Daniel Kalt, the chief economist of UBS investment bank, can forecast financial data and present investment advice to high-wealth customers (www.nanalyze.com). Overall, digital human avatars seem most effective for building long-term customer relationships in contexts that feature substantial complexity or risk (e.g., financial investments), where users prioritize realistic, trustworthy, and personalized service.

Observations from business practices suggest that avatars' effectiveness may be highly contingent on the level of perceived uncertainty users experience during their interactions with avatars. This uncertainty might arise from contextual factors, such as functional risk, financial risk, or price ([25]). Functional risk refers to the concern that the product/service may fall short of performance expectations. Financial risk refers to a possible loss of money, independent of purchase price, due to a poor decision (e.g., stock performance) ([25]). In addition, as the purchase price increases, the need for information about the quality of products or services also becomes more important to manage perceived risk ([102]). Overall, we predict that when customers feel greater uncertainty, they develop heightened expectations that an avatar that offers a realistic anthropomorphic appearance will also have a comparable level of behavioral realism, because they rely on the avatar's informativeness and ability to provide personalized advice to reduce the perceived risks associated with the purchase.

• Proposition 5: The positive effect of an avatar's form realism on customers' behavioral realism expectations is stronger when (a) functional risk is higher, (b) financial risk is higher, and (c) the product is more expensive.

We previously posited that when form realism exceeds behavioral realism, the avatar can have both positive (via customer's affective responses) and negative (via customer's cognitive and social responses) effects on performance outcomes. These mediated effects also might be moderated by perceived uncertainty. When perceived uncertainty is high, the avatar's entertainment value may become less salient to the consumer because an entertaining avatar cannot overcome the perceived risks associated with the purchase. The negative disconfirmation induced by the avatar's lack of behavioral competence should become more salient and detrimental to consumers' cognitive and social responses, resulting in weaker performance outcomes. For example, whereas the realistic, anthropomorphic appearance of HSBC's Amy seems effective because she was programmed to provide basic information to routine customer questions that do not involve high-risk transactions, the very realistic appearance of Nordnet's Amelia could not compensate for her lack of competence to offer stock advice (i.e., high financial risk transactions). Thus, we expect the following moderation effects:

• Proposition 6: When an avatar's form realism exceeds its behavioral realism, its positive effect on customers' affective responses is weakened if ( 1) functional risk is higher, ( 2) financial risk is higher, and ( 3) the product is more expensive.
• Proposition 7: When an avatar's form realism exceeds its behavioral realism, its negative effects on customers' cognitive and social responses are strengthened if ( 1) functional risk is higher, ( 2) financial risk is higher, and ( 3) the product is more expensive.

Conversely, when an avatar's behavioral realism exceeds its form realism, it may have a stronger positive effect on customer performance outcomes under high perceived uncertainty. This is because an avatar's form realism may induce a positive disconfirmation regarding its behavioral competence, which can reassure customers of the usefulness of the information and personalized service provided by the avatar, thereby boosting customers' confidence in their risky purchase decisions. Another pertinent risk in today's world is that of privacy violations ([25]). If an avatar's behavioral realism exceeds its form realism, the avatar provides reassurance that customers are dealing with an intelligent, nonhuman entity that will not judge them, so it should mitigate social unease and embarrassment. The PTSD therapist Ellie works with highly private matters, involving patients disclosing emotionally and psychologically sensitive information. Such situations might evoke greater concerns if patients cannot tell whether they are dealing with a real human therapist behind the screen.

• Proposition 8: When an avatar's behavioral realism exceeds its form realism, its positive effects on customers' cognitive and social responses are stronger if (a) functional risk is higher, (b) financial risk is higher, (c) the product is more expensive, and (d) privacy risk is higher.

As avatars are also increasingly used in mobile apps (e.g., ING's Inge, Progressive's Flo, Bank ABC's Fatema), the choice of mobile or fixed devices as platforms on which the firm decides to provide an avatar also may be pertinent. Compared with fixed devices (e.g., desktops), mobile devices are particularly entertaining as hedonic information technologies (e.g., video games, MP3 players) have made their way into these portable devices ([103]). Moreover, consumers spend more time in online communities when they use mobile rather than fixed devices ([66]), suggesting that mobile devices are the preferred channel for online social experiences. Thus, avatars that are entertaining and capable of establishing personalized social connections with customers may be especially effective on mobile devices.

• Proposition 9: As form realism increases, relative to behavioral realism, the use of mobile devices (compared with fixed devices) strengthens the positive effect of avatars on customers' affective responses.
• Proposition 10: As behavioral realism increases, relative to form realism, the use of mobile devices (compared with fixed devices) strengthens the positive effect of avatars on customers' social responses.

Finally, the effects of customers' cognitive, affective, and social responses on performance outcomes may depend on the consumer relationship phase, which refers to the relational trajectory of a customer–seller exchange ([29]; [77]). During consumer–avatar interactions, three relationship phases are particularly important: exploration, build-up, and maturity ([47]). During the exploration phase, a consumer is primarily concerned with the potential value and benefits of dealing with the seller, so the perceived informativeness of the avatar becomes especially critical. During this initial stage, entertainment or social engagement provided by the avatar might even detract from the consumer's task objectives and compromise customer outcomes. In this stage, avatar design should allow for behavioral competence, so that the avatar can provide accurate, task-specific, customized information rather than focusing on designing highly attractive or socially engaging avatars. Such an approach should produce a positive disconfirmation for customers' cognitive experience. As the relationship proceeds to the build-up phase, the consumer has experienced some benefits from interactions with the firm's avatars, so socialization processes (e.g., building rapport) become more important. Engaging and fulfilling social experiences can help customers develop long-term commitment to the firm. A positive disconfirmation about the avatar's ability to deepen social bonds might prove especially effective. Finally, during the maturity phase, a consumer satisfied with the cognitive and social benefits of interacting with an avatar may focus less on these factors and instead seek more entertainment value, prioritizing affective responses.

• Proposition 11: The effects of customers' cognitive, affective, and social responses on performance outcomes are moderated by the relationship phase, such that (a) the effects of cognitive responses are amplified in the exploration phase but suppressed in the build-up and maturity phases, (b) the effects of affective responses are enhanced in the maturity phase but suppressed in the exploration and build-up phases, and (c) the effects of social responses are strengthened in the build-up phase but weakened in the exploration and maturity phases.

To synthesize these insights, we offer an integrated framework of avatar performance (Figure 3). This framework provides a visual summary of key insights from our review of extant research and business practices, as well as our development of the avatar taxonomy and propositions. With this framework, we work to advance thought in this emerging, contemporary marketing area by integrating three mediation mechanisms (customers' cognitive, affective, and social responses) together with theory-driven moderators to test theory as well as offer managerial implications.

Graph: Figure 3. An integrated framework of avatar performance.

Our integrative analyses of academic research and business practices generate practical implications and research directions for avatar-based marketing, which we group into five key managerially relevant areas: ( 1) when to deploy avatars, ( 2) avatar form realism, ( 3) avatar behavioral realism, ( 4) form–behavioral realism alignment, and ( 5) avatar contingency effects (Table 3).

Graph

Table 3. Managerial Implications and Research Directions for Avatar-Based Marketing.

In terms of when and where avatars should be used, avatars seem to be most effective in service-oriented industries (e.g., financial, travel, telecom services), in which the sheer scale of service requirements and customer inquiries can easily overwhelm a company's employees. Avatars can free up employees' time, so that the employees can focus on complex customer needs and offer more value-added services, with greater productivity. Avatars can provide consistent, personalized service and help build emotional connections between the firm and its customers ([22]; [52]). For companies that serve a large portfolio of customers, avatars can also make it feasible to launch a segmented, multichannel strategy. By offering customers opportunities to engage with avatars through different channels (e.g., social media, company websites, dedicated apps), the firm ensures that it meets each customer's unique needs at the right time and in the right place.

After confirming that an avatar should be implemented, the firm should determine the design of avatar's appearance, with the clear recognition that its form realism is a double-edged sword. On the one hand, a more realistic, anthropomorphic appearance appeals to consumers, because it offers greater entertainment value ([78]). On the other hand, it elevates customers' expectations of the avatar's behavioral competence, which is much more difficult and costly to develop. If the avatar's behavioral competence falls short of customers' expectations, they experience a negative disconfirmation, which can decrease their satisfaction. To avoid negative performance outcomes, an avatar's anthropomorphic appearance should not exceed the level of its behavioral competence.

Having decided on the avatar's appearance, companies can design the avatar's behavioral competence. If companies lack the resources to develop high form–high behavioral realism avatars (i.e., digital human avatars), they should allocate more resources to the avatar's behavioral intelligence than to its appearance. A positive disconfirmation of the avatar's cognitive and social competence likely produces an above-average level of customer satisfaction or even customer delight ([34]), which can increase firm performance.

Managers should account for form realism–behavioral realism alignment too. If an avatar has high levels of both form and behavioral realism, customers' high initial expectations about the avatar's behavioral performance will be confirmed. Because customer satisfaction is an additive function of ( 1) initial expectations of the avatar's behavioral competence and ( 2) subsequent confirmation or disconfirmation of this expectation ([76]), the high form realism–high behavioral realism alignment will likely produce high levels of affective, cognitive, and social responses in consumers, as well as better outcomes overall. Additional research is needed to determine the "zone of tolerance" and the precision required when aligning form and behavioral realism of avatars.

Finally, in addition to considering uncertainty factors and media channel choice, design efforts should take the customer relationship phase into account, because the relative effects of customers' cognitive, affective, and social responses differ across relationship stages. For example, during the exploration phase, a positive confirmation regarding an avatar's behavioral realism can ensure good cognitive experiences (e.g., cognitive trust), but during the maturity phase, a more entertaining avatar (e.g., funny, attractive appearance) may prove more effective for sustaining the relationship. Future research that takes a lifecycle approach to avatar design and use could determine avatar effectiveness at each stage and the strategies needed to adapt in accordance with customers' dynamic needs.

Our analysis of avatar design strategies indicates some promising research opportunities. First, propositions derived from our conceptual framework provide opportunities for empirical research, which can be tested using different methods. For example, researchers might collaborate with an avatar design company to manipulate form and behavioral realism in a 2 × 2 full-factorial experiment, consistent with our taxonomy in Figure 2. These results would provide evidence of the effects of form realism on expectations for behavioral realism (P₁), as well as (dis)confirmations induced by any (mis)alignment (P₂). Moderation tests also might be carried out with lab experiments that allow for manipulations of uncertainty factors (e.g., risk, price, privacy) or channels (e.g., mobile app vs. desktop) (P₅–P₁₀). To test the mediation effects, researchers might use a difference-in-differences field experiment to examine the aggregate main effect. Using a low form realism–low behavioral realism avatar as a baseline (control group), researchers could increase form (treatment 1 group) and behavioral (treatment 2 group) realism. Any significant differences in daily sales across the treatment groups and control group, between the pre- and posttreatment periods, would indicate the external validity of the asymmetric effects of form realism–behavioral realism misalignment (P₃, P₄). To confirm the distinct mediation effects, customers also could be surveyed regarding their cognitive, affective, and social responses, shortly after the treatments, followed by tests of the effects on performance outcomes (e.g., purchase intentions, word of mouth). Alternatively, a lab experiment can be used for differential mediation tests. As for the test of the relationship phase (P₁₁), a cross-sectional survey posted on a retail website that uses an avatar might enable researchers to conduct subgroup analyses or moderated regressions to detect differential effects of mediators across relational phases. A more demanding and rigorous approach would secure panel data from a collaborating retailer that uses avatars and agrees to let the researchers track customers' relational trajectories through longitudinal surveys, while also granting them access to objective customer sales data.

Research opportunities also exist in areas that our framework does not cover. Avatars are designed to enhance the productivity of company employees, rather than replace them altogether, so continued research might investigate how to optimize avatar–human collaborations, especially if problems arise. Various approaches allow customers to switch to a human representative when necessary, using avatar-initiated, employee-initiated, or customer-initiated "exit ramps." These approaches can differ in their nature (proactive vs. reactive) and timing (early vs. late in the interaction). Thus, determining how and when human intervention is introduced could provide significant insights for achieving service recovery and ensuring customers' overall satisfaction with and commitment to the firm. In this article, we have highlighted the influence of an avatar's form realism on the overall customer experience. Additional research should uncover which specific dimensions of the avatar's anthropomorphic appearance exert the strongest impacts on customers' behavioral realism expectations, which form realism elements create the most entertaining avatar experience, or when a digital assistant without an anthropomorphic appearance (e.g., Amazon's Alexa) would perform better than an avatar. For example, if the avatar's form realism exceeds its behavioral realism, which subset of behavioral elements is most likely to lead to customers' expectation disconfirmation? What corrective actions would be effective in addressing negative disconfirmations? Research also could delve deeper into the effects of avatars' emotional intelligence, relative to their cognitive intelligence, in shaping customers' expectancy (dis)confirmations and overall experience.

Another type of avatar that is growing in popularity is customers' self-avatars, created with virtual model technology ([89]). To inform research into these applications, the typology we developed would need to be modified to reflect the unique characteristics of self-avatars (e.g., resemblance to self). Some designer brands (e.g., Gucci) have successfully engaged customers to dress their self-avatars in branded products, then share them on social media ([16]). The limited research on self-avatars has focused primarily on enclosed online environments (e.g., retailer's website), not social media ([20]; [35]), indicating the pressing need for insights into how, why, and when self-avatars could perform in social media marketing campaigns.

Brands also have turned to virtual influencers (3D, computer-generated personalities) instead of or in addition to human influencers for online marketing campaigns. Powered by advanced AI, these avatars can attract significant followers on social media platforms. For example, with almost 3 million Instagram followers, Lil Miquela has endorsed brands such as Prada and Calvin Klein ([ 1]; [ 6]). This avatar actively replies to social media comments, appears in publications like Vogue, and even participates in live media interviews ([19]). While virtual influencers offer unique benefits to brands such as content control and versatility, they also create potential risks. For example, 61% of consumers assert that authentic, relatable content is the primary appeal of human influencers ([80]), but only 15% of followers of virtual influencers describe them as credible ([21]). Moreover, because the virtual influencer avatar is not a human, the brand it endorses ultimately is held responsible for its actions. Academic research has yet to investigate the unique benefits, risks, and operational mechanisms associated with avatar-based virtual influencer marketing.

Future research might also explore avatar-based targeting strategies. Demographics, psychographics, and benefits sought are widely used customer segmentation bases ([95]); they also might be used to predict which customers will be best served by a given type of avatar. For example, customer's demographic traits might interact with the avatar's demographic attributes or behavioral elements to influence customer's cognitive, affective, and social responses to the online experience. Creating a personality or decision-making style for an avatar that matches those of the customer might be an effective, psychographics-based design strategy ([ 4]). Segmenting markets on the basis of in-depth analyses of the motives that lead certain people to interact with avatars could also inform benefits-based avatar deployment strategies ([11]). Distinct mediation mechanisms could be uncovered across different customer segments to inform idiosyncratic avatar designs. Research is also needed to determine when avatars may distract from, rather than contribute to, the customer's experience, as well as find strategies to address these challenges. Finally, to extend beyond our focus on the relative effects of the different types of avatars established by our taxonomy, future research might compare the effects of avatars with the impacts of other digital representations such as emoji, anthropomorphized products, brand mascots, or voice-only digital assistants. Understanding when and how avatars, versus these alternative marketing tools, are more effective in influencing online shopping experience and performance outcomes demands further scientific inquiry.

Rapid increases in the use of avatars have been fueled by two main factors: advances in digital technologies and increasing reliance on online experiences among both consumers and firms. The use of avatars is projected to grow by 35% annually ([41]). However, the effectiveness of avatars continues to be uncertain, so we offer an integrated theoretical framework to establish definitional and conceptual clarity, synthetize academic research and business practices, and offer insights and propositions that provide managerial implications and an agenda for future research. The proposed definition of avatars, as digital entities with anthropomorphic appearance, controlled by a human or software, with an ability to interact, helps us establish a design typology, which in turn gives academics and managers insights into how to isolate elements that make avatars more or less effective for specific goals.

We synthesize academic literature and business practices by offering a 2 × 2 form realism–behavioral realism taxonomy, which in turn enables us to derive propositions regarding the effectiveness of avatars in marketing. The level of alignment between an avatar's form realism and behavioral realism, according to several contingencies, can provide a parsimonious explanation of when an avatar will be most effective. With insights gained from our investigation of fundamental avatar elements, extant research, and business practices, we develop an integrative framework of avatar performance that offers theoretical insights, research propositions, managerial implications, and future research directions.

Supplemental Material, sj-pdf-1-jmx-10.1177_0022242921996646 - An Emerging Theory of Avatar Marketing

Supplemental Material, sj-pdf-1-jmx-10.1177_0022242921996646 for An Emerging Theory of Avatar Marketing by Fred Miao, Irina V. Kozlenkova, Haizhong Wang, Tao Xie and Robert W. Palmatier in Journal of Marketing

Supplemental Material, sj-pptx-1-jmx-10.1177_0022242921996646 - An Emerging Theory of Avatar Marketing

Supplemental Material, sj-pptx-1-jmx-10.1177_0022242921996646 for An Emerging Theory of Avatar Marketing by Fred Miao, Irina V. Kozlenkova, Haizhong Wang, Tao Xie and Robert W. Palmatier in Journal of Marketing

1 Cait Lamberton

2 The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

3 The authors acknowledge the financial support of the National Natural Science Foundation of China (grant #71832015; 72072191)

4 Online supplement: https://doi.org/10.1177/0022242921996646

5 Related terms included animated agent, anthropomorphic agent, artificial agent, chatbot, conversational agent, digital assistant, electronic shopping agent, embodied agent, embodied virtual agent, nonhuman agent, spokes-avatar, spokes-character, virtual agent, virtual assistant, and virtual character.

6 The academic disciplines included were (1) marketing (e.g., Journal of Marketing), (2) computer science (e.g., Computers in Human Behavior), (3) information systems (e.g., Journal of Management Information Systems), (4) communications (e.g., Human Communication Research), (5) education (e.g., Computers & Education), (6) health care (e.g., Journal of Medical Internet Research), and (7) general business (e.g., Journal of Business Research).

7 We acknowledge there may be exceptions in which a very "cute" avatar, although low in form realism, might provide strong hedonic value. We thank an anonymous reviewer for this insight.

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By Fred Miao; Irina V. Kozlenkova; Haizhong Wang; Tao Xie and Robert W. Palmatier

Reported by Author; Author; Author; Author; Author

Analyzing the Cultural Contradictions of Authenticity: Theoretical and Managerial Insights from the Market Logic of Conscious Capitalism 

This research analyzes the cultural contradictions of authenticity as they pertain to the actions of consumers and marketers. The authors' conceptualization diverges from the conventional assumption that the ambiguity manifest in the concept of authenticity can be resolved by identifying an essential set of defining attributes or by conceptualizing it as a continuum. Using a semiotic approach, the authors identify a general system of structural relationships and ambiguous classifications that organize the meanings through which authenticity is understood and contested in a given market context. They demonstrate the contextually adaptable nature of this framework by analyzing the authenticity contradictions generated by the cultural tensions between "conscious capitalism"—a market logic that encompasses both global brands and small independent businesses, such as a farm-to-table restaurant or an organic food co-op—and the elitist critique. The Slow Food movement provides a case study for analyzing how consumers, producers, and entrepreneurs who identify with conscious capitalist ideals understand these disauthenticating, elitist associations and the strategies they use to counter them. The authors conclude by discussing implications of the analysis for theories of authenticity and for managing the authenticity challenges facing conscious capitalist brands.

Keywords: authenticity; brand image; conscious capitalism; ethical consumerism; consumer identity; market logics; semiotics

Consumers crave authenticity—so much so that their quest for authenticity is considered "one of the cornerstones of contemporary marketing" ([11], p. 21). This has created an enormous challenge for the field, considering that marketing itself is typically considered inherently inauthentic. —[67], p. 1)

In the field of marketing, little doubt exits that "authenticity" is highly desired by consumers and thereby is a crucially important strategic resource for marketing management. Consumers are more likely to form stronger emotional attachments to a brand, business, or tourist site they perceive as being authentic ([20]; [30]; [57]; [86]) and to incorporate these market resources into their identities ([ 7]; [11]; [45]). On the managerial side, [31], p. 610) conclude that authenticity is "the most rare and coveted asset in the contemporary branding landscape." Their assertion is supported by an array of studies indicating that authenticity is integral to the enhancement of brand equity ([60]), effective brand extensions ([82]), persuasive marketing communications ([ 5]), success in relationship marketing ([22]), and emotionally engaging person and celebrity brands ([31]; [87]).

Although there is a clear consensus that authenticity profoundly matters to both consumers and marketers, the marketing literature also presents a recurrent concern that authenticity is a nebulous concept that has eluded precise definition ([ 5]). [67], p. 2) proclaim that this conceptual ambiguity poses a significant barrier to creating "a coherent theory of authenticity." Accordingly, they aim to redress this dilemma by presenting a general definition of authenticity based on six key perceptual components. In contrast, [81], p. 3) proposes that "authenticity is a polysemous and multilayered concept" and thus "it might not [emphasis added] be helpful to compress the wealth of disparate meanings associated with the concept into a single definition."

As Södergren further notes in his meta-analysis, "the majority of the research [on authenticity] has focused on characteristics that distinguish the 'real thing' from the fake" (p. 11). To further elaborate on this conceptual tendency, marketers' efforts to define authenticity almost invariably invoke some variant of genuineness, such as brands (via their management teams) staying true to ideals of timeless tradition, heritage, craftsmanship, and quality (see also [ 6]; [82]). In this spirit, [55], p. 30) propose that the authenticity of a brand's social media communications hinges on perceptions of honesty, sincerity, and being "real." [ 7] similarly contend that the core cultural meanings of authenticity are truth, genuineness, and reality. [67] comprehensive definition of authenticity also incorporates a series of veracity-oriented constructs, such as originality (i.e., not being a copy), accuracy (i.e., being true to others), and integrity (i.e., being true to oneself).

While the analytic goal of distinguishing the authentic from the inauthentic makes intuitive sense, it is a Sisyphean undertaking that attempts to specify an ambiguous cultural category by referring to other semantic terms whose meanings are also contextually contingent and malleable (i.e., honesty, sincerity, originality, genuineness, and truthfulness). Furthermore, informing marketing managers that their brand lacks authenticity because consumers see it as being unoriginal, insincere, or dishonest offers little guidance on how to resolve the deeper cultural tensions that drive these unfavorable perceptions. Rather than a checklist of definitional attributes, we argue that marketing managers need an analytic approach that can enable them to answer questions such as ( 1) why is their brand or business susceptible to certain kinds of authenticity challenges?, ( 2) what cultural meanings and contradictions underlie those challenges?, and ( 3) what responses could they take to mitigate the disauthenticating associations that ensue from these tensions?

Returning to our opening vignette, we can reframe [67], p. 1) statement that marketers face an "enormous challenge" because their profession is "typically seen as inauthentic" (see also [ 5]) as a realization that marketing, as a business practice, also occupies an ambiguous cultural position. On the one hand, marketing aims to advocate for the needs (and voices) of customers ([41]) and, yet, it is also means for companies to enhance their profits and market share. This tension readily gives rise to concerns that short-term (and potentially exploitive) profitability goals might take priority over serving customers' best interests. Accordingly, consumers are inundated with cultural narratives (ranging from journalistic reports about deceptive marketing tactics to portrayals of unscrupulous marketers by entertainment media) that encourage cynicism and distrust toward marketers' branding claims and persuasive communications ([32]; [45]; [66]).

However, the specific cultural meanings and associations that lead to perceptions of authenticity or inauthenticity vary across brands and markets. For example, consumers are likely to deploy different configurations of meanings, beliefs, and evaluative norms when judging the authenticity of a high-fashion retailer ([23]), a café owner who promotes their establishment as a home-away-from-home ([20]) or a global brand that positions itself as an advocate for environmental justice (e.g., Patagonia; [49]).

In this article, we explain and demonstrate how the semiotic square ([40]) can be used to systematically analyze such culturally heterogeneous authenticity contradictions and to develop contextually appropriate responses to the specific authenticity challenges that arise in a given market. The semiotic square is an analytic tool that has often been used to delineate cultural meanings and semantic contradictions that are manifest in both consumer perceptions and marketing strategies ([29]; [36]; [50]; [51]; [54]; [68]; [69]). From a semiotic perspective, the cultural categories of the authentic and the inauthentic are not just contrasting or oppositional terms. Rather, they are anchor points in a broader network of relationships through which the authenticity of a given brand, business, brand ambassador, social media influencer, and the like is culturally constructed and potentially contested.

Our market context is conscious capitalism, which refers to a "way of thinking about capitalism and business that better reflects where we are in the human journey, the state of our world today, and the innate potential of business to make a positive impact on the world" ([62], p. 273). Conscious capitalism is particularly vulnerable to the broader authenticity–inauthenticity tension that all marketers confront to varying degrees. Therefore, it serves as a very relevant and informative context for our analysis.

Conscious capitalism's key premise is that capitalism's societal purpose has, historically, been defined too narrowly (i.e., maximizing shareholder wealth and optimizing consumers' market choices) and, accordingly, its society-enhancing potential remains greatly underutilized. Rather than grafting a social mission onto a traditional profit-maximization model, as per conventional corporate social responsibility approaches, proponents of conscious capitalism contend that businesses should place value-driven goals and social consciousness at the core of their institutional missions ([62]).

By aiming to redefine the nature and function of capitalism, conscious capitalism can be analyzed as a market logic that transcends its iconic brands (e.g., Patagonia, Starbucks, TOMS, Whole Foods) or socially conscious businesses (e.g., a cooperatively owned, fair trade, local coffee shop). As discussed by [27], pp. 40–42), a market logic is an integrated network of meanings, values, and norms that provide ( 1) principles that can guide thoughts, actions, and preferences; ( 2) vocabularies of motivation and justification; and ( 3) material and symbolic resources for constructing an identity (such as being an ethical consumer or a purpose-driven business owner).

Conscious capitalism organizes a constellation of ideologically aligned brands and an even larger network of businesses that have different scales of operation and serve different roles in the supply chain. Thus, consumers who support this array of brands and enterprises have access to a set of normative principles to guide their purchase choices (e.g., locally sourced materials are preferred over imported ones, plastic product packaging should be avoided); they learn an intricate system of terms and codes (e.g., "postconsumer recycled content," third-party certification labels such as the Rainforest Alliance or Certified Carbon Neutral); and they can express their socially conscious sensibilities through an array of consumption practices—wearing a Patagonia fleece, driving an electric car, shopping at a farmers' market, brandishing a reusable Whole Foods' canvas tote bag, buying fair trade chocolate, or supporting a farm-to-table restaurant.

In the general public discourse, however, the authenticity of conscious capitalist brands and businesses, and their consumer supporters, is frequently called into question. These authenticity challenges are sufficiently problematic that leading proponents of conscious capitalism feel compelled to address them:

There is a growing network of people building their companies based on the idea that business is about more than making a profit. It's about higher purpose ... and the innate potential of business to make a positive impact on the world.... But one of the most predictable responses we get from people when we mention the idea of conscious capitalism is, "That's an oxymoron!" ([61])

Conscious capitalism's authenticity challenges hinge on a cultural tension between the profit-maximizing ethos of capitalism and the ennobling idea that capitalist enterprises can serve higher societal and moral purposes that supersede commercial interests ([ 3]; [33]). In this vein, critics often suggest that conscious capitalism deploys the language of sustainability and other socially beneficial goals for the instrumental purpose of catering to higher-income consumers who will pay a premium to imbue their consumption practices with an aura of moral virtue:

Thus, the rise of social enterprises [i.e., conscious capitalist enterprises] has been met with hostility, particularly toward its authenticity and its sustainable impact. If their goods and services continue to be priced as they are, is the sustainable movement only for the demographic that can afford it? ([14])

[70], p. 73) similarly argue that conscious capitalism is more promotional hyperbole than a viable business reality and, further, add this reservation: "It is important to note that the firms associated with the Conscious Capitalism movement are far from a random sample of American businesses: In fact, a great many sell relatively expensive products to relatively affluent, socially- or health-conscious consumers."

This incredulous and, at times, adversarial public response to conscious capitalism has not arisen ex nihilo. Rather, it draws from a cultural narrative that we characterize as the "elitist critique." As historian [34] elaborates, the political charge of elitism has evolved from its classic populist roots, which railed against the undue power wielded by the captains of industry and affluent political insiders, to an antipathy toward the intellectual class (who may not be unduly wealthy or politically powerful). Through this shift, the charge of elitism was distanced from its origins in economic conflicts between the working class and the owners of capital (and their management intermediaries) and became repositioned in a culture war rift whereby "the 'elite' could be identified by its liberal ideas, coastal real estate, and highbrow consumer preferences" ([34], emphasis added).

We investigate how the specific authenticity challenges posed by the elitist critique of conscious capitalism are negotiated by consumers and producers in the context of the Slow Food movement ([89]). Slow Food encompasses an array of ideologically aligned brands, enterprises (farm-to-table restaurants, artisan producers, and organic and free-range farmers), consumption practices (e.g., shopping at a farmers' market or a local co-op), and goods and services (e.g., an heirloom tomato, grass-fed beef, a class in fermentation techniques). Slow Food's signature issues and social change goals are grounded in the market logic of conscious capitalism, including local sourcing, fair wages for workers, sustainable modes of production, environmental awareness and habitat protection, and a broader project of redressing societal ills through the coordinated actions of socially conscious businesses and consumers (see [73], [74]). The elitist critique has also become part and parcel of Slow Food's brand image, and it poses salient authenticity challenges for Slow Food's producers, entrepreneurs, and consumers.

In the following sections, we first discuss the key analytic premises of the semiotic square. Next, we develop a semiotic conceptualization of authenticity that maps out its structural contradictions (and ambiguous classifications). We use this analytic framework to explicate the ways in which the elitist critique gives a particular cultural form to the authenticity contradictions plaguing the market logic of conscious capitalism. We then profile the authenticating strategies that Slow Food advocates (consumers, producers, and restauranteurs) use to counter these disauthenticating elitist associations. We conclude by discussing the implications of this analysis for theories of authenticity and for managing the authenticity challenges facing conscious capitalist enterprises.

From a semiotic perspective ([40]), the meaning and categorical boundaries of a given concept are defined through relations to what it is not. For example, the cultural meanings of masculinity have been historically established through contrasts to those that have defined femininity and the related nexus of ever-changing ideals, values, and practices through which this binary contrast has been culturally articulated and transformed over time ([50]). These structural relations give rise to ambiguous categories whose associated cultural meanings can become points of contestation and debate, such as in the cases of "metrosexuals" ([78]), stay-at-home dads ([19]), or the ongoing controversies sparked by the category of transgender athletes ([12]).

The binary opposition between authenticity and inauthenticity presents a similar arrangement of contradictions and ambiguous classifications. Consequently, we propose that authenticity is not a set of discrete properties that distinguish the genuine from the fake—but, rather, an ongoing process of managing a network of contingent relationships. In some markets, for some brands and enterprises, these contingencies may be more stable, whereas in others, they may become more culturally contested and, thus, unstable. We suggest that conscious capitalist brands and businesses, owing to the elitist critique, exemplify this latter and more managerially challenging case.

Figure 1 presents a semiotic square representation of authenticity. In this article, we use the "contradictions of authenticity" as an integrative term that encompasses the structural relations among the semiotic 3Cs (contrariety, complementarity, and contradictory relations).

Graph: Figure 1. A semiotic model of the authenticity–inauthenticity opposition.

The horizontal arrows represent contrariety relations. These relations are roughly analogous to the standard binary oppositions that anchor semantic differential scales. However, relations of contrariety further indicate that the meaning of a term is defined through a relationship to its binary contrast (e.g., good is understood relative to evil). Accordingly, the meaning of authenticity is always contingent on the operative meaning of inauthenticity, and vice versa. We refer to the authentic ↔ inauthentic contrariety as the primary contrariety relation because it represents the dominant tension that, in turn, sets the complementary terms for the secondary contrariety relation (i.e., not inauthentic ↔ not authentic).

The vertical arrows represent complementarity relations. Such conceptual pairings are compatible and noncontradictory (but are not necessarily synonymous or interchangeable). For example, "not inauthentic" is congruent with the dominant term, authentic. However, this classification also harbors other connotations and, thus, ambiguous meanings. For example, imagine a painting created by a famous artist, say Picasso, who at the time was a fledgling beginner, imitating the style of another painter. Because the painting does not evince Picasso's quintessential artistic motifs, its authenticity becomes ambiguous (and debatable)—that is, at what point in his career does a painting by Picasso truly become a "Picasso"? The term "not inauthentic" conveys this type of ambiguity.

The diagonal arrows represent contradictory relations. These relations indicate that any entity or action deemed to be authentic (or inauthentic) will harbor some qualities that can be judged as contradicting such an assessment. As an illustration, let us again consider the idea of artistic authenticity. From a conventional standpoint, the authenticity of an artist, even a renowned one, can always be challenged on the grounds that their creations exhibit properties that are derivative of other genres, styles, or artistic predecessors (authentic ↔ not authentic). Conversely, the art world's postmodern movement disavows the idea of artistic originality and, instead, celebrates that all artistic productions are, in some sense, a reworking of something prior. As exemplified by Andy Warhol's replications of iconic cultural images (Coca-Cola bottles, Campbell Soup cans, the face of Marilyn Monroe), postmodern art is also heralded for its capacity to surprise and inspire revelatory aesthetic experiences through its creative (and often ironic) uses of repetition, collage, assemblage, montage, and bricolage (inauthentic ↔ not inauthentic) ([43]).[ 4]

In Figure 1, the cloud-like drawings represent the specific cultural meanings that give contextual form to the contradictions of authenticity. For purposes of our analysis, the relevant meaning systems are the market logic of conscious capitalism and the elitist critique. This system of semiotic relationships gives rise to four emergent (and ambiguous) classifications, each harboring latent contradictions. In discussing these ambiguous categories, we first illustrate them in more general terms and then address their manifestations in the context of conscious capitalism and the elitist critique.

This complementarity relation corresponds to what [39] discuss as indexical authenticity. In this usage, an index refers to a given object or behavior—for example, the actions of a whitewater raft guide, handprints in front of Grauman's Chinese Theater, a painting, or a branded good. Indexes are classified as authentic when they are believed to possess a factual and spatiotemporal connection to some validating condition. For example, consumers will judge the actions of a whitewater raft guide as authentic if they are believed to reflect an inner passion for the outdoors (rather than being a calculated performance done for remunerative purposes; [ 2]). Similarly, consumers will typically deem a branded good to be authentic when they believe its design, production, and quality certification has proceeded under the auspices of those who own or manage the brand of note.

As these examples suggest, perceptions of indexical authenticity can be more or less certain. As an example of higher certainty, Prada certifies the genuineness of its handbags by assigning each a unique and traceable serial number that is documented on an authenticity card. On the less certain side, customers have to infer the indexical authenticity of a whitewater raft guide's passion for the outdoors or a retail associate's expressions of friendliness and interpersonal concern. In these cases, consumers' judgements about the authenticity (or inauthenticity) of a marketer's actions (or the actions of other consumers) depend on their inferences about underlying motivations and intent.

The elitist critique provides a constellation of culturally shared meanings and rationales that support disconfirming suppositions about the indexical authenticity of conscious capitalist enterprises and their consumer followers. These disauthenticating associations directly correspond to the ambiguous categories emerging from the primary contrariety (authentic ↔ inauthentic), the secondary contrariety (not inauthentic ↔ not authentic), and the complementarity relation of inauthentic ↔ not authentic relations.

This ambiguous classification corresponds to seemingly oxymoronic constructions such as authentic reproductions—or, in semiotic vernacular, "iconic authenticity" ([39]). In this usage, the icon is an object that is a known facsimile of an original referent and that is appreciated for its mimetic properties, as in the case of a comedian doing an uncanny impression of a celebrity. For the category of iconic authenticity, the ensuing goal is to present a compelling sense of verisimilitude through a meticulous recreation of the original referents' characteristics. Iconic authenticity is pursued by, among others, members of the cosplay community ([80]) and consumers who perform in historical recreations, such as Civil War reenactments ([17]). In a different market application, iconic authenticity would also be highly relevant to a budget-conscious consumer who wants to buy a convincing counterfeit version of an expensive designer brand.

When situated in the context of the elitist critique, the "authentic + inauthentic" category assumes less favorable meanings of moral pretentiousness and hypocrisy. In this disauthenticating cultural frame, affluent (and typically left-leaning) consumers use conscious capitalist brands and goods to distinguish themselves from the price-conscious mainstream and their socioeconomic peers who display affluence through more ostentatious lifestyle choices ([24]). By claiming the mantle of moral virtue, such consumers can pursue social distinction in an otherwise orthodox manner—that is, through material displays of refined tastes ([ 9]; [44])—while appearing to disavow materialism and status consciousness.

For example, during its heyday as a cultural icon, the Toyota Prius inspired oppositional brand communities (Muñiz and O'Guinn 2001) who referred to the vehicle (and its drivers) as "the pious." This epithet suggested that Prius drivers evinced a self-aggrandizing "holier than thou" stance that amplified the moral merits of their automotive preferences relative to those who made different choices ([59]). In a similar cultural vein, [13] note that ecofriendly consumers who purchase organic foods, drive electric luxury cars, and use natural cleaning products typically lead lifestyles that carry a much higher carbon footprint than lower-income consumers who live in smaller housing units, rely on public transport, and seldom fly. Seen in this critical light, such ecoconscious (affluent) consumers are virtue signaling ([24]; [42]; [90]), but their pretense of moral superiority is not warranted by these symbolic acts.

This ambiguous classification highlights that perceptions of genuineness (often taken as the sine qua non of authenticity) are a necessary but not insufficient condition for ascribing this honorific appellation to an object or action. That is, an entity or action may be deemed as genuine (i.e., not fake) but lack the perceived aesthetic or moral virtues needed to be classified as "authentic," or, conversely, to have its authenticity challenged. Thus, we can have marketplace conditions where authenticity, in its full moral and aestheticized sense, is not a relevant cultural category.

To illustrate, barring extenuating circumstances, consumers seldom venerate conventional mass-produced goods (e.g., a Big Mac, a Gillette disposable razor) for their authenticity because they lack potent associations with rarefied aesthetic ideals. Conversely, such items are not typically classified as inauthentic either (assuming that they are not knock-off products), with companies often promoting the standardized nature of their branded offerings—and the resulting performance consistency—as value-added benefits.

In the context of the elitist critique, the "not inauthentic + not authentic" classification suggests that middle-class consumers who support conscious capitalist brands and enterprises are, owing to their class privileges, inherently "not authentic." This disauthenticating implication hinges not on conscious intent but on the systemic advantages afforded by their relatively privileged socioeconomic position. Rather than being hypocritical per se (i.e., authentic + inauthentic), the implication is that such consumers may genuinely believe that conscious capitalism is a viable means to create a more equitable and just society. However, their genuine belief is an ideological one, steeped in their internalized class interests. Middle-class consumers' ideological affinity for the market logic of conscious capitalism allows them to lead a materially privileged lifestyle in a guilt-free manner ([92]). By purchasing brands and goods that signify a heightened social consciousness (e.g., fair trade coffee; TOMS shoes; organic, locally sourced foods), they can feel symbolically absolved from culpability in the perpetuation of socioeconomic inequalities. Consequently, their habituated class predilections also create an ideological blind spot toward the exclusionary signals that conscious capitalist ideals and values convey to those who lack the economic and cultural resources needed to fully participate in a middle-class lifestyle ([47]).

This ambiguous classification suggests that conscious capitalists' products, services, and brands are, to use[66] term, "gimmicks" that always promise more than they can deliver. [21], p. 668) further discuss issues relevant to this classification in their typology of transactions. Among their designations of deceitful transactions (i.e., scams), they list "fraud" and "confidence games." In the former condition, a disingenuous party misrepresents their intentions to an unsuspecting partner; in the latter condition, the scammer actively enrolls their target in the ruse, such as in catfishing and pyramid schemes.

The inauthentic + not authentic classification implies a manipulative opportunism whereby an unethical agent feigns genuineness to extract ill-gotten gains from another. In the context of conscious capitalism, this disauthenticating association is most germane to the marketer side of the exchange. As one well-known example, the business ethics journalist Jon Entine accused the pioneering conscious capitalist brand The Body Shop, and its founder Anita Riddick, of fraudulent misrepresentation. According to [25], Riddick stole the brand concept from a local entrepreneur and fabricated an authenticating origin story about traveling the world searching for natural skin care and hair treatments. His exposé further contended that The Body Shop significantly overstated the percentage of profits that it donated to philanthropic causes. Though Riddick formally denied these charges, the authenticity challenges posed by these accusations, as well as others that subsequently followed, continued to plague the brand ([76]). After years of underperformance, relative to the brand's prescandal pinnacle, The Body Shop undertook a revitalizing strategy that its management characterized as an activist revamp ([77]).

The "inauthentic + not authentic" classification can also cast more nuanced doubts on the authenticity of conscious capitalist entrepreneurs. Although such conscious capitalist entrepreneurs would not be committing overt acts of fraud (i.e., they are not lying about their business practices per se), the disauthenticating implication is that they are cynically espousing higher-order civic ideals to serve commercial ends, such as charging a premium to their consumers or driving higher stock valuations. This disauthenticating association can arise, for example, when the founder/chief executive of a privately owned conscious capitalist brand sells its rights to a larger corporate entity. Such a backlash arose when Gene Kahn—the founder of Cascadian Farms—sold his business to General Mills. Many leading voices in the organic food community lambasted Kahn's integrity, condemning him as a Boomer sellout and warning that the brand's corporate ownership would not stay true to the higher-order values that originally galvanized the organic food movement (see [75]).

To investigate how Slow Food advocates negotiate the elitist critique of conscious capitalism and its disauthenticating connotations, we recruited informants from a Slow Food chapter located in a metropolitan area of the Midwestern United States using informational flyers, contacts made at local chapter meetings, and snowballing referrals. We conducted interviews at public locations such as coffee shops or at Slow Food–sponsored events, with exception of two that respectively occurred in these participants' domestic residence and private work office. Interviewees were paid $20 in appreciation for their time. Interviews were audiotaped and ranged from one to four hours in duration, yielding 830 double-spaced pages of verbatim text. All participant names are pseudonyms.

Of our 19 interviews, 8 were conducted with chapter organizers, 5 with Slow Food producers and entrepreneurs, and 6 with Slow Food advocates who had volunteered their time to different outreach activities (for our participants' profiles, see Table 1). Most of our Slow Food organizers and consumer advocates are college graduates employed in professional occupations and hail from middle- and upper-middle-class families. Among the entrepreneurs, Dave, Leslie, Maggie, and Tom are also college graduates. This demographic profile matches the membership ranks of Slow Food USA, which skews toward middle-class professionals ([16]).

Graph

Table 1. Participant Profiles.

1 Notes: M = male; F = female; B.A. = bachelor of arts; B.S. = bachelor of science; M.A. = master of arts; MBA = master of business administration; M.S. = master of science; Ph.D. = doctor of philosophy.

Following the conventions of in-depth phenomenological interviewing ([85]), our participants largely determined the course of the dialogue. The interviewer relied on follow-up probes to elicit more detailed accounts of the informants' experiences and viewpoints and to ensure that various aspects of food production, distribution, and consumption were covered. Procedurally, our interpretation developed through an iterative process of creating, challenging, and reworking provisional understandings by tacking back and forth between individual transcripts and the broader data set ([84]). We then pivoted to another level of hermeneutic tacking that entailed iterations between these emic narratives and theoretical concepts, which led us to the application of the semiotic square and our resulting focus on the elitist critique and corresponding strategies for countering the authenticity challenges posed by the cultural contradictions manifest in this market system.

As an institutional entity, Slow Food is a transnational organization encompassing 1,500 local chapters plus numerous subsidiary organizations. Beyond its formal institutional boundaries, Slow Food's culinary practices, values, and activist goals organize ideological and economic alliances among a globally diffused network of food writers (such as Mark Bittman and Michael Pollan), consumers, producers, merchants, and restauranteurs (including celebrity chefs Alice Waters and Jamie Oliver). As [18], p. 131) writes, "The phrase 'slow food' strikes a chord among the public not because it is the name of an organization but because it reflects a series of desires, interests and concerns."

Slow Food discourses valorize meals that are traditionally prepared with fresh ingredients as unique sources of pleasurable experiences that can mobilize consumers to resist the industrialized system of food production. Over the years, the Slow Food movement has embraced a broader conscious capitalist agenda that advocates for sustainable production, environmental protection, and social justice (i.e., fighting hunger, advocating for living wages for agricultural workers; see [16]; [89]).

Like other conscious capitalist exemplars, Slow Food has also been plagued by charges of elitism from its inception in 1986 when its founder, Carlo Petrini, organized a series of public protests over the opening of a McDonald's in the heart of Rome (see [89]). This ignominious view of Slow Food finds ready expression in both academic analyses (e.g., Guthman 2007; [56]) as well as journalistic accounts, such as [38], who states that "none of the aggressive, judgmental pitches of the movement have ever been proven. The power of its association with the economic elite has."

From this skeptical standpoint, Slow Food's exalted rhetoric of sustainable diets, biodiversity, and socially conscious eating (see https://www.slowfood.com/about-us/our-philosophy/) is a guise for privileging upper-middle-class tastes over the dietary practices of less affluent (and lower-cultural-capital) consumer segments (see [53]; [56]). Even Slow Food's ardent proponents, such as food writer Annie Levy, concede that a tacit elitism has hindered the cultural diffusion of its core principles:

—The revered Alice Waters once said, "when we eat food that is fast, cheap, and easy, we digest those very values." What are the judgments contained in this kind of statement? She intends, I believe, to critique the values of a food system that doesn't care about its conditions or effects on people and the environment. But the words suggest that if you eat fast, cheap, and easy you become fast, cheap, and easy—language many women might recognize as shaming. Isn't this how it really sounds to someone who enjoys such food, or is caught in situations in which it might seem the best available option? ([58])

Slow Food encourages consumers to shift their culinary tastes away from fast food and industrialized fare (including the oft-demonized category of junk food) ([16]; [83]). Such admonitions can imply a moralistic hectoring and an invidious comparison with those whose food tastes and practices are more orthodox. These elitist associations often cross into other sociocultural spheres, such as the controversy sparked by former First Lady Michelle Obama's school lunch initiative, which was institutionalized through the Healthy, Hunger-Free Kids Act of 2010. By explicitly disavowing fast food and processed foods, the revised school lunch guidelines dovetailed with Slow Food's mobilizing agenda—an alliance that Slow Food USA was eager to promote (see Figure 2).

Graph: Figure 2. Fodder for the elitist critique: Slow Food's controversial political alliances.

Once these Slow Food–friendly standards went into effect, news (and social) media began to feature anecdotal reports of children refusing to eat these presumably unpalatable lunches and skyrocketing food waste ([28]), with some critics characterizing the program as "gastro-fascism" ([71]). The elitist charge became integral to this cultural (and political) backlash:

Michelle Obama thinks she knows what your children should eat. She's adamant about promoting her nutrition policies for kids, even the new and disastrous school meal standards implementing the "Healthy, Hunger-Free Kids Act."... But attending Ivy-League schools doesn't magically make someone better parent material than an individual who attended a public university, or, dare it be said, someone who didn't attend college. ([ 4])

Like other market exemplars of conscious capitalism, Slow Food's aesthetic and experiential arguments have become strongly associated with an unwarranted moralism (i.e., the authentic + inauthentic classification; primary relation of contrariety). Slow Food advocates frequently argue that fast food is a debased cuisine that deprives humanity of meaningful and rewarding experiences of eating and sociability ([73]; [83]). For the many consumers who have warm memories of enjoyable fast-food meals with friends and family (and maybe look forward to such treats), Slow Food's moralizing pronouncements seem to emanate from an elitist taste bubble that is disconnected from everyday pleasures and real-world practicality. Similarly, Slow Food's veneration of locally sourced ingredients, heirloom vegetables and grains, artisan-crafted foods, and seasonal cuisine also seems to assert an unwarranted claim to moral virtue. Rather than sacrificing for a greater societal good, such rarefied culinary objects seem more attuned to signaling that one possesses the discretionary resources of time and money to treat food and cooking as a self-actualizing identity practice. Accordingly, Slow Food is easily, via the elitist critique, decried as an aggrandized form of cultural snobbery (e.g., [53]).

Figure 3 represents the correspondences between Slow Food's contextualized authenticity contradictions, the disauthenticating association that ensues from each contradiction, and the strategies through which Slow Food advocates seek to negate these authenticity challenges. In this representation, indexical authenticity (authentic ↔ not inauthentic) is the contested ideal that our Slow Food advocates are seeking to defend.

Graph: Figure 3. Authenticity contradictions and authenticating strategies in the Slow Food market.

Our Slow Food consumers place the most emphasis on the reflexive strategy, which they use to counter the authentic + inauthentic contradiction (primary relation of contrariety) and its disauthenticating association of virtue signaling and moral pretentiousness. Rather than rejecting the elitist critique outright, Slow Food advocates interpret it as a warning sign that the Slow Food market has become a gentrified facsimile of the movement's origins in the everyday cuisines of rural Italians (i.e., a disparaging version of iconic authenticity). Accordingly, our participants revere practices that seem to resurrect Slow Food's agrarian values and democratizing goals.

The humanistic rebel strategy redresses the not inauthentic + not authentic contradiction (secondary relation of contrariety) and its disauthenticating association of social exclusion. In the context of the elitist critique, this contradiction holds that individuals whose lives have been shaped by class privilege may be blithely unaware of their own internalized elitist predispositions. From this standpoint, Slow Food advocates may have a genuine interest in making the world a better place (i.e., they are not consciously "faking it"; rather, they are being "not inauthentic"). However, they are largely oblivious to how their viewpoint on these problems and solutions has been shaped by a life of class privilege and their habituated, middle-class (bourgeoisie) sensibilities. This disauthenticating association renders Slow Food consumers as being somewhat akin to the proverbial fish in water. Rather than not realizing they are wet, however, the analogical implication is that they cannot comprehend that other terrestrial animals lack the requisite resources to enjoy life in the water, as they do.

To negate this authenticity challenge, our participants drew from humanistic rationales, such as the idea that certain kinds of experiences and social connections have magical and transformative qualities that transcend social differences ([ 2]). Importantly, this strategy combines a humanistic ethos with the idea of rebelling against a deleterious marketing and cultural status quo and, thereby, creates a distinction to the complicit, part-of-the-problem connotations of liberal elitism (see [92]).

The perfective strategy corresponds to the inauthentic + not authentic contradiction (relation of complementarity). This strategy is most relevant to those positioned on the entrepreneurial/production side of this market system. It aims to negate the disauthenticating association of commercialism (i.e., conscious capitalist enterprises are profit-seeking marketing ploys). In response, our Slow Food producers and entrepreneurs draw from the bohemian ideal of the artist who refuses to compromise their artistic vision, despite market incentives to "sell out" (i.e., betraying one's artistic integrity in return for financial reward) ([10]; [87]). Accordingly, they present themselves as being intrinsically committed to perfecting their Slow Food craft and pursuing conscious capitalist values and ideals, rather than doing it for the money.

Slow Food advocates use the reflexive strategy to negate the authenticity challenge of moral pretentiousness. The implication is that Slow Food assigns an unwarranted degree of moral virtue to those who have the economic wherewithal to buy rarefied ingredients, spend time on complex meal preparations, and dine at expensive farm-to-table restaurants while casting those who lack such resources as less virtuous consumers. In response, our participants interpret Slow Food's cultural associations with affluent foodies and elite taste practices as a regrettable, but correctible, market distortion of the movement's authentic values and practices.

While acknowledging that market upscaling has imbued Slow Food with an elitist aura, our participants reiterate that expensive, epicurean cuisine need not be and, indeed should not be, regarded as the quintessential expressions of Slow Food:

I think one of the things is this perception that if you shop at farmers' markets or at the co-op, it's a lot more expensive. And there is a little bit of this Slow Food bent into cooking elaborate meals, and I think some people perceive that as being elitist because it's sort of this educated way of thinking about food. I don't think of it as being elitist because a lot of times, recipes can be super expensive to buy all the ingredients for, but they don't have to be. I don't think that enjoying your food should be something that is thought of as elitist.... Like, I buy what's not super expensive at the co-op and I cook pretty simply.... What I really like about Slow Food in particular is the aspect of enjoyment and that good food is for all—what good, fair, clean food means for the farm worker to the people who are consuming the food. (Erin)

Erin does not summarily dismiss the elitist charge. Rather, she takes a more ambivalent stance by first conceding that Slow Food values and ideals are often enacted in ways that can be read as elitist, such as cooking elaborate meals using expensive ingredients. In her authenticating interpretation, she counters that Slow Food values are better expressed through fundamentally simple meals that do not require extensive preparation time or costly ingredients. Her emphasis on there being many affordable options at her food co-op (which is, of course, a relative judgment) and on "cooking simply" (another relative assessment) convey that she is staying true to Slow Food's core principles rather than trying to place superficial, foodie predilections on a higher moral plane.

Erin further counters this aspect of the elitist critique by incorporating the economic interests of farmers into her inclusive interpretation of Slow Food stakeholders. This interpretation creates a rhetorical contrast between Slow Food's foundational discourse of economic populism (emphasizing fair wages for agricultural workers) ([73]) and the elitist condemnation that higher prices are merely a means for affluent consumers to mark status distinctions.

Paula's narrative exhibits a similar authenticating logic to that expressed by Erin:

Slow Food has often come under fire for being elitist. I don't actually think that's true.... The beginnings of Slow Food were about people eating good food, and those were not necessarily rich people. We are talking about people who might have had very little money.... When most people think about amazing Italian cuisine, they were eating very basic foods. So, the whole idea of eating good food to me doesn't seem elitist at all.... Slow Food in the United States, yes, we do certain things that might be seen as elitist—the restaurant dinners and stuff like that. But again, you are still educating people. You are still getting more people involved. And the more people who know about local farming, sustainable farming, eating seasonally, making sure that farm workers are protected and paid properly, that spreads out. And we do projects with a variety of different populations, and we are trying to do more of that.... Slow Food does a lot of work in all its chapters to help with urban gardens or school gardens.... In the long run, our goal is that all people have access to this kind of food.... We are working toward passing that power on to more people. So, I don't think wanting children and families in need to have high-quality food is elitist. (Paula)

In this vignette, Paula first differentiates Slow Food practices from elitist pretentions by invoking its historical connections to rustic Italian foodways. She asserts that Slow Food enjoins a pleasurable, resourceful, and fundamental relationship to food that should be accessible to people from all walks of life, rather than being an exclusive province of affluent consumers. However, Paula also recognizes that her inclusive rendering of Slow Food is contradicted by the realities of socioeconomic stratification. From Paula's viewpoint, Slow Food's community outreach efforts can play a pivotal role in democratizing these forms of culinary cultural capital so that consumers from less privileged backgrounds can acquire the skills and knowledge needed to incorporate Slow Food practices and ideals into their culinary routines.

When utilizing this reflexive strategy, Slow Food advocates routinely assert that cultural capital ([ 9]), rather than a lack of economic resources per se, is the primary barrier that keeps consumers from integrating Slow Food ideals and practices into their everyday lives. Christina echoes this rationale when discussing how low-income consumers could enact Slow Food practices if they had more knowledge about utilizing the fresh produce and bulk goods that often go to waste in the local food pantry where she volunteers:

Some people who are in Slow Food are foodies. However, it does not cost a lot of money to eat right. There are food pantries who throw away produce because people who come to the pantry don't know what to do with it and they don't take it.... Fresh produce going to waste.... No one wants it because they don't know what to do with it. It's really unfortunate. So, people have more access than they think. There are bulk aisles at grocery stores that you can get food for less money. It is actually a lot less expensive to buy bulk rice or bulk oats or whatever else, than to buy the bagged, boxed stuff that's like creamy preprocessed. I think the real lack of resource is education, not so much money. (Christina)

Slow Food advocates often draw an authenticating contrast between foodie-ism—which fetishizes highly aestheticized meals prepared with exotic (and typically expensive) ingredients (see [52])—and the Slow Food ethos of preserving traditional foodways and skills ([75]). This distinction is quite salient to Slow Food chapter leader Kevin. He posits that Slow Food's culinary values and ideals have become misconstrued in their translation to the consumerist, status-conscious culture of the United States. Kevin's goal is to reclaim Slow Food's original ethos from its commercial appropriation by high-end retailers and restaurants:

To buy imported cheese, organic wine, and all these kinds of things, I don't think those are meant to be the most obvious expressions of Slow Food values. And I think this is where the cultural translation from Italy to the United States went wrong, is that it got tied up with those folks [i.e., affluent foodies]. In Italy, it's much more about cooking at home. It's much more about preserving grandma's recipes. It's much more about celebrating the seasons and the tradition and preserving home ways of life than it is about eating in restaurants that do everything right. And you know, like anything else, capitalism wants to subsume this revolution.... That's a schism that I am personally trying to address and maybe lead by example. I don't think we should be cooking like a Michelin-starred restaurant at home. I think we should be cooking like our grandparents and great-grandparents. And I think we can learn a lot from traditional cultures and indigenous people—to the extent that there still are any indigenous people—how to eat well, and you know, a lot of those foods have become an affectation in restaurants. They'll have poutine, but it's made with truffles, and confit duck and elaborate things. I have realized that we are all attracted to the comfort foods and the simple foods, like tacos, and they are easy and fun to make. (Kevin)

For Kevin, Slow Food should be accessible, basic, fun, and easy—characteristics that diverge from associations with rarity, cosmopolitan sophistication, aesthetic refinement, and technical proficiency that mark elite tastes ([44]). If read in a more critical light, Kevin's narrative reiterates the nostalgic glossing of preindustrial foodways that critics of the Slow Food movement assail for ignoring the harsh realities of scarcity and subsistence endured by those who had to survive on "traditional diets" ([56]).

While romanticizing images of a bucolic culinary past have considerable appeal to our Slow Food advocates, the idea of rekindling a premodern utopia is not that central to the reflexive strategy's authenticating function. Rather, these homages to a bygone era, when people lived close to the land and prepared food in traditional ways, symbolically link Slow Food practices to agrarian and/or rural lifestyles far removed from elite pretensions:

I did an internship through Worldwide Working on Organic Farms.... I went to Italy and I milked sheep and goats for a couple of months. And it was very rural. It was very low-tech. We milked in buckets by hand, sheep and goats, and we kind of went out with big sticks and sheepdogs and herded them.... Slow Food originated in Bra, Italy, and that was only like an hour and a half away from the farm. So, I think that's kind of how Marco [the farmer] was involved in Slow Food. He made cheese that was very well regarded, and he went to cheese festivals and stuff. But I mean the whole day was slow. Like wake up kind of late, drink your espresso, milk leisurely, walk the mount, you know. Dinner took a really long time, but that was kind of okay. And just kind of do the same things over and over again. So, we all cooked together. They also did some kind of agro-tourism. They'd have people from the city come out and we would cook with them the food we either grew or found.... That was fun. (Leslie)

Leslie's narrative validates a nexus of Slow Food ideals regarding small-scale production and the slower pace of agrarian life. While this rural setting has some trappings of a staged performance—most notably Marco's side business in agro-tourism—it places Slow Food in a symbolic sphere far removed from the Whole Foods brandscape, expensive farm-to-table restaurants, and other consumption domains invocative of foodie affectations (see [52]). For Leslie, her story of interning on a rustic Italian farm affirms that she has actually lived the conscious capitalist values she endorses through her Slow Food advocacy (and thus is not a hypocritical moralizer). More generally, consumer narratives that link Slow Food practices to traditional modes of food production, down-home family meals, and simpler ways of living—rather than exorbitantly priced gourmet dishes—express a rhetorical parry to the elitist charge of moral pretentiousness.

Our Slow Food advocates use the humanistic rebel strategy to redress the authenticity challenge posed by the elitist critique's connotation of social exclusion and the related sociological argument that consumers' social class backgrounds structurally predetermine their taste affinities ([44]). From this critical viewpoint, Slow Food advocates may not consciously intend to be elitists, but their preferences for goods that convey meanings of sustainability, locavorism, and artisanship betray a host of class advantages that distinguish them from consumers whose lives are marked by conditions of necessity ([47]). While Slow Food advocates may be well intentioned (i.e., they are being "not inauthentic"), they are also complicit in a system of institutionalized class-based inequities.

To illustrate this tension, let us reassess Leslie's preceding vignette in relation to this association with social exclusion (rather than moral pretentiousness). On the one hand, working for room and board on a small, rural farm clearly diverges from conventional notions of status posturing. However, a sociological counterpoint is that Leslie—as a college-educated young adult engaging in an exploratory experience—is building a reservoir of life stories and cultural capital that can afford career and status advantages later in life (see [91]). Seen in this sociological light, Leslie is enacting her class privilege by having the economic and social latitude to intern on a rustic, Italian farm before transitioning into more conventional middle-class occupational pursuits, such as attending graduate school.

As a chapter leader, Kevin has oriented his local chapter's activities toward the goal of making Slow Food a more inclusive organization that does not merely cater to the interests of middle-class consumers. When implementing these outreach projects, however, Kevin recognizes that many Slow Food practices are simply incompatible with the situational demands that lower-income consumers have to negotiate on a daily basis:

I have amazing privilege,... like being an American to a middle-aged white guy who has very marketable skills.... But I realize that that is a privilege, and this is the biggest thing for us in Slow Food to grapple with—that a single mother who has three jobs and two kids doesn't have the luxury of deciding, "I think I would like to work less and spend more time in my garden".... So, you have to be very careful and sensitive about it. (Kevin)

Kevin believes that this class chasm can be bridged if his team of middle-class volunteers presents Slow Food's approach to food provisioning, cooking, and eating in a manner that is "sensitive" (i.e., adapted) to the lifestyle constraints faced by less well-resourced consumers. His optimistic viewpoint is based on the authenticating assumption that Slow Food's experiential and social benefits have an inherent appeal to consumers, regardless of their class position, because they tap into fundamental human desires and needs.

The humanistic rebel strategy takes this inclusive rationale further by suggesting that a confluence of technological and commercial forces have locked individuals into an accelerating pace of life. Consequently, experiences of social connection, spontaneity, and everyday small pleasures are sacrificed to demands for efficiency, convenience, and the seductive (and ultimately alienating) effects of social media and digital communications.

Aaron echoes this humanistic rebel mantra in his commentary on the inherent importance of eating and cooking with others:

You have the social component of Slow Food as cooking and eating together and taking time to reflect and to connect and to develop community. That's something that we lose when we have things like drive-through or microwave dinners, which aren't to be destroyed or demonized altogether. I take advantage of these services of society. But for them to be the baseline means that we are losing what ... enriches our social systems a lot more than people eating alone and interacting through screens.... So, I think food, when it's jointly cooked and eaten, serves as a very natural medium for connection and idea generation and creativity. (Aaron)

Aaron characterizes Slow Food as a needed corrective to societal transformations in the practices of cooking and eating that have resulted in a loss of sociability, communal bonding, and creative interactions. By interpreting Slow Food as a medium for enjoining meaningful social interactions, Aaron expands its cultural province well beyond the predilections of affluent consumers. Aaron's caveat that he has partaken in the conveniences afforded by fast food and microwave meals is another rhetorical means for distancing his Slow Food preferences from elitist connotations. His narrative signals a conscious disavowal of snobbery by acknowledging that there is a legitimate time and place for "fast food." Aaron then specifies the problem as the cultural ubiquity of fast food, which, in turn, he links to dehumanizing consequences.

When expressing the humanistic rebel strategy, our participants often couched the communal experiences of preparing or eating food as magical moments that affirm Slow Food's class-transcendent qualities:

I ran a cheese-making class, and that was really fun because I've always been interested in cheese-making, just for the fun of it. There were seven or eight people there. And one of the members still is making cheese today. And it was really fun to be able to share that magic with people. That this is how this cheese actually comes into being, and it's totally doable by yourself at home. So, that's really exciting, that he got so inspired. It's fun to see somebody get really interested in something. (Amanda)

Amanda emphasizes the magic of cheese-making and the inspiration and rewarding personal experiences of self-sufficiency it can enjoin (i.e., "totally doable by yourself at home"). Her narrative expresses a cosmological view of nature as a magical, life-transforming force ([ 2]) whose authenticating properties are not inherently tied to class-shaped tastes. In keeping with this formulation, Amanda interprets the sharing of her Slow Food skills as a means to help others experience these inspiring connections to nature, which, in turn, implies a revelatory contrast to the alienated experiences of industrialized fast food.

Maggie similarly interprets her self-taught Slow Food skills as a means to help people create a sense of communal togetherness and to experience new sensory pleasures and magical connections to the land:

I think of Slow Food as taking your time to respect the ingredients and preparing them from scratch and enjoying food. And that, to me, resonates. And bringing back the social aspect of eating. Like, you take time to prepare this meal, you sit down, you share it with people who care about the same things that you do. And it's also, creating another community of people who value these things whether they're growing, or cooking, or eating; having that kind of common thread, I think is really satisfying. (Maggie)

Maggie venerates Slow Food as being inherently conducive to experiences of sociability and community and as a way to recognize the meaningfulness of food (a normative orientation that reads as a general human value, rather than a class-interested practice). However, Maggie is a meat producer who, like other Slow Food entrepreneurs, faces an additional task of negating contradictions that derive from the "inauthentic + not authentic" category.

The perfective strategy seeks to negate Slow Food's disauthenticating association with commercialism. This aspect of the elitist critique casts Slow Food producers and entrepreneurs as disingenuous actors who are enrolling consumers into an inauthentic market relationship (akin to a gimmick or a confidence game) to serve their own economic interests. In response, our Slow Food entrepreneurs strive to authenticate their actions by signaling that they would never compromise their Slow Food ideals for the sake of profit, such as by recounting the copious amounts of time and energy they invest into perfecting their Slow Food enterprises

In this spirit, Tom, a farm-to-table restauranteur, views his business as a way to enact his passionate commitment to producing food in a more meaningful and socially beneficial way:

When you go to a fast-food restaurant, you have no idea of who actually made that food and the process of where it came from is not known to you. The taste and flavor are mostly engineered to play off the cheap sensory sensations. So, it's fatty and salty and sweet and so, yeah, on a certain level, it might be gratifying, but it's a cheap way to do it that is less meaningful. Slow Food is like, "We're going to do things in a way that is process oriented!" I talk about process a lot.... We [Tom and his restaurant staff] were really structured around learning, and so it was a process where we feel like we've excelled and learned a lot and we'll keep pursuing that.... [With Slow Food,] you have this process where people are eating and making something and understanding where it came from and how it works. Eating is such an important part of our lives, and it can have a really important impact on our community and environment. So, the more you understand about it, hopefully you'll make better decisions. The basic motto of Slow Food is clean, fair, and good food. I can totally get behind those values. (Tom)

During his interview, Tom extensively discussed "the process" aspects of his restaurant and how he views it less as a business than a means to cultivate and diffuse knowledge about the complex interrelationships among food, cooking, sensory enjoyment, ecology, and societal well-being. His quote also reiterates Slow Food's argument that the experiences of these simple culinary pleasures can mobilize consumers to resist the industrialization of the food system (thus echoing the humanistic rebel strategy). For Slow Food entrepreneurs, however, it serves the additional function of associating their enterprises with civic goals that stand distinct from conventional commercial aims.

Returning to Maggie, she raises pasture-fed rabbits for sale to farm-to-table restaurants and consumers. In developing her production techniques, Maggie has constantly experimented with different procedures and equipment designs. Through this long trial-and-error process, Maggie believes she has developed an innovative method that better simulates the lives her rabbits would enjoy outside of captivity:

Maggie: Daniel Salatin is the son of Joel Salatin, who is the owner of Polyface Farms, and he is the person who is raising rabbits in this system that he has devised and calls the Hare Pen system. So essentially, you still have your does in cages.... You put them in a glorified cage that you then put on grass.... I've copied their system exactly, and I was very unsatisfied with the results that I got. [Maggie then provides an extensive description of her alternative and labor-intensive system and how she developed it]... I don't know why I kept doing it. But I finally have a system that is really effective.... It just was a lot of observation of the rabbits on pasture, making so many mistakes and then incorporating what I had learned.

Interviewer: Did you have any economic incentives?

Maggie: No! It has to be a personal belief that there might be a better way to do things.... It's kind of like what makes an artist a good artist. If they all hold the brush the same way and they are using the same colors, but they create vastly different things, and one appeals to you, and one doesn't appeal to you. So, what makes that one piece of art recognized by the vast majority of people as superior?... I have this wonderful platform to invest energy and creativity, and it's nice. And so, I feel in some ways really lucky.

Invoking the image of the passionate artist, Maggie distinguishes her efforts to perfect an ecologically appropriate system for raising rabbits from crass commercial and economic interests. Maggie's closing sentiment expresses her authenticating belief that such actions can make things better, rather than being driven by instrumental aims. Through storytelling, and by showing how her system works to customers who visit her farm, Maggie deploys narrative and material resources to negate disauthenticating concerns that her Slow Food affinities are merely an instrumental means to charge higher prices. Her personal investment in learning about rabbits' natural habitats/behaviors and inventing a complex ecosystem for raising them further signals that she is not likely to compromise her Slow Food principles in the interest of commercial expediency.

Prior research has treated authenticity as a perceptual value or quality that consumers attribute to a brand ([11]; [55]), person-brand ([87]), product ([57]), or performance ([ 5]; [39]). In contrast, we have reconceptualized authenticity as an ongoing process through which consumers and marketers negotiate a contextualized system of cultural contradictions and ambiguous classifications. We suggest that our semiotic framework can better analyze the authenticity contestations that arise in a given market or sociocultural context than conventional theories that assume authenticity perceptions operate on a continuum or selectively draw from an essential set of defining attributes.

The conceptualization of authenticity as a relative point along an authentic-to-inauthentic continuum ([22]; [65]) can depict a zone of ambiguity where the authenticity or inauthenticity of a market actor is perceived as being uncertain and, thus, debatable. However, this conceptualization does not offer a means to specifically analyze the cultural meanings (and the interrelationships among them) that generate these ambiguous perceptions. Accordingly, it offers limited theoretical discrimination and managerial guidance.

For example, [65] argue that quality commitment, heritage, and sincerity are the primary perceptual cues of authenticity. They then propose that brands should differentially leverage these cues depending on whether consumers perceive them as having low, moderate, or high levels of authenticity. In their normative framework, brands with low perceived authenticity should emphasize sincerity, brands with moderate perceived authenticity should emphasize quality and heritage, and brands with a high level of perceived authenticity should emphasize all three authenticity cues.

Such recommendations presume that brands falling into the lower and middle sectors of this proposed continuum have a shortfall of perceived quality commitment, sincerity, or heritage that is rectifiable through compensatory signaling. However, such contested brands are often plagued by contradictory meanings that undermine their promoted claims to authenticity ([37]; [86]). Furthermore, more complex, disauthenticating narratives, such as the elitist critique, can cast doubt on the very credibility of such authenticating cues when used by a contested brand or actors in a market system.

Turning to combinatory definitions, [67] have offered a comprehensive theorization of authenticity (as understood from the consumer's perspective) that warrants comparison to our approach. They identify six subdimensions of authenticity (accuracy, connectedness, integrity, legitimacy, originality, and proficiency) and then trace out the relative impact of those dimensions across different market categories and on consumers' behavioral intentions. Rather than a continuum, Nunes, Ordanini, and Giambastiani argue for a family resemblance explanation in which "a concept (authenticity, in this case) may be qualified by different subsets of its dimensions across different contexts, and not always by all of them in the same way" (p. 16).

Like a continuum, [67] family resemblance logic is limited to the explanation that authenticity is a multidimensional construct whose subcomponents may be more or less important in a given market or consumption context. In contrast, a semiotic framework shifts attention from correlational premises (e.g., this authenticity subdimension seems more important for hedonic products than utilitarian ones) to the cultural meanings, and underlying structural contradictions, relevant to a particular judgment regarding the authenticity of a given product, brand, or market action. For example, the authentic ↔ inauthentic tension elevates the importance of authenticity's moral dimensions in ways that traverse product category distinctions, such as hedonic or utilitarian.

To illustrate, a hamburger would typically be classified as a hedonic good. [67], pp. 3–4) find that judgments of "legitimacy"—which they define as "the extent to which a product or service adheres to shared norms, standards, rules, or traditions present in the market ... appear to matter for utilitarian but not hedonic products." However, if we examine this consumer choice in the context of the Slow Food market, then legitimacy becomes a far more important issue. From this standpoint, an "authentic burger" would need to exhibit fidelity to various aesthetic and moral norms—grass-fed beef, local sourcing, traditional preparation techniques, and so on—and, its perceived authenticity would be understood and legitimated through a contrast to fast-food burgers. That authenticating contrast (the fast-food burger vs. a Slow Food burger) could then become subject to the elitist critique, which, in turn, would provide motivation for Slow Food advocates to negate these disauthenticating associations.

In summary, we have argued that authenticity is culturally constructed (and contested) in a network of structural relations (rather than being a discrete set of essential properties attributed to a brand, person-brand, market performance, or market relationship). Consumers and marketers alike covet indexical authenticity (i.e., the abstract ideal of authenticity) because it can confer cultural legitimacy ([51]), moral authority ([59]), and identity validation ([ 7]; [86]) all of which, can be converted into micro-celebrity status ([79]) and a branding asset ([31]; [45]). However, this authenticity ideal is structurally linked to contradictory meanings and ambiguous classifications. When consumers' or marketers' authenticity claims are challenged by these cultural contradictions, they have pressing incentives to distinguish their actions and identities from the invoked disauthenticating associations. In the following subsection, we discuss how this authenticating goal can be enacted by negating associations that flow along the contradictory path of deception and promoting those that follow the contradictory path of redemption.

As [49] have argued, marketing managers often find it difficult to redress brand image problems because they are unable to effectively decipher the cultural meanings contributing to those dilemmas. Our semiotic framework can help redress this managerial shortfall. It offers a tangible means for marketing managers to systematically analyze the cultural contradictions of authenticity that emerge in a given market and then to identify strategies for authenticating their brands in the face of these challenges.

As a general heuristic, we propose that marketers can be successful in authenticating their brands and/or other strategic assets when they are able to accomplish two complementary goals. The first is to leverage cultural meanings that negate the disauthenticating associations that flow along the contradictory of deception path (authentic → not authentic; see Figure 1). When consumers follow this perceptual path, they experience a glaring contradiction between a prevailing ideal of authenticity and its market manifestation in a brand or marketing practice (authentic ↔ not authentic), which then leads to an association of inauthenticity via the complementary relation of not authentic → inauthentic. In response, marketers should try to provide consumers with compelling and emotionally resonant meanings and rationales that discount the credibility, relevance, or importance of the disauthenticating associations that have gained cultural currency in their respective market.

As one illustration, Patagonia confronted a path of deception authenticity challenge soon after it began campaigning against the Trump administration's executive order to reduce the size of Utah's Bears Ears National Monument by two million acres. On December 4, 2017, Patagonia featured this message on the front page of its website: "The President Stole Your Land: In an illegal move, the president just reduced the size of Bears Ears and Grand Staircase-Escalante National Monuments. This is the largest elimination of protected land in American history." This web page then directed consumers to various information sources and encouraged consumers to contact their elected officials and to also take the protest to social media, using the hashtag #MonumentalMistakes (see [ 1]).

However, defenders of the administration's policy change were quick to denigrate Patagonia's activism as a deceptive marketing ploy. Interior Secretary Ryan Zinke condemned Patagonia as a dishonest "special interest" and proclaimed it was "shameful and appalling that they would blatantly lie in order to put money in their coffers." Utah Representative Bob Bishop, then chairman of the House Natural Resources Committee, also evoked the elitist critique in his tweet proclaiming that "Patagonia is Lying to You... A corporate giant hijacking our public lands debate to sell more products to wealthy elitist urban dwellers from New York to San Francisco" (quoted in [35]).

In terms of our model, the Trump administration's response challenged the authenticity of Patagonia's mobilizing campaign by impugning its motivations, thereby reframing an ostensibly authentic (conscious capitalist) action as a disingenuous public relations stunt designed to extract more profits from elite consumers (authentic → inauthentic), which, in turn, triggers the complementary association to inauthenticity. In response, Patagonia joined as a coplaintiff with five Native American tribes and several nonprofit groups in a lawsuit aiming to halt the policy change ([35]; see also https://www.patagonia.com/stories/hey-hows-that-lawsuit-against-the-president-going/story-72248.html). Patagonia also continued to be a vocal critic of the Trump administration's environmental policies and, in a politically and ideologically related vein, donated all its tax savings from the Trump-backed corporate tax cut to environmental groups while condemning the new corporate tax rates as being irresponsible ([63]). Through these responses, Patagonia signaled a deeper commitment to its conscious capitalist values and gave consumers reasons to doubt or dismiss the disauthenticating associations of greed and deception that were being cast on it. In response to Patagonia's uncompromising stance, Inc. offered the following commentary on its 2018 Company of the Year finalist:

For Patagonia and its fans, that purpose is doing whatever they can to try to save the planet. In 2018, Patagonia proved that it will not only preach that mission, it will do so with a much louder voice than most other companies. And—so far, anyway—it's only further burnished the Patagonia brand. ([ 8])

The second marketing goal is to create conditions in which consumers interpret a brand or business along the contradictory of redemption path (inauthentic → not inauthentic) (see Figure 1). This redemptive chain of associations begins with the widespread cultural view of marketers as inauthentic and, thus, untrustworthy actors ([45]; [66]; [67]). Accordingly, we can assume that consumers will typically harbor varying degrees of skepticism and suspicion toward the authenticity of marketing and branding claims. Redemptive meanings encourage consumers to believe that a given brand or business is operating in ways that favorably diverge from the marketing status quo (i.e., not inauthentic) which, in turn, leads to the complementary relation of not inauthentic → authentic.

Volkswagen's (VW's) "Hello Light" advertisement, which launched its new line of electric vehicles (circa 2019), takes viewers on a journey that follows a path of redemption arc (see https://www.youtube.com/watch?v=qEvNL6oEr0U). The ad begins with a silhouetted figure entering a dark and seemingly abandoned production facility, while a news report about VW's emission scandal, or "Dieselgate," blares in the background. In a seemingly counterproductive marketing communications move, the ad explicitly reminds its viewers of all the inauthentic associations (VW as liar, deceiver) that arose from those "dark" days. The protagonist is revealed to be a despondent engineer struggling to design a new VW model, against the musical backdrop of Simon and Garfunkel's 1960s anthem, "The Sound of Silence." Desperate for inspiration, our engineer scours the company archives and finds his creative muse—an image of the iconic VW Van (aka the "Love Bus").

Through the choice of song and reference to this totem of the 1960s counterculture, the ad recalls VW's countercultural legacy as an authentic symbol of antimaterialist values and a rebuke to status consciousness and marketing hype ([46]). The ad's message is that VW, despite having lost its way, still possesses a latent essential "goodness" that is "not inauthentic." As "The Sound of Silence" reaches its crescendo, lights go on, puncturing the darkness. We observe the production facility come to life and give metaphorical rebirth to the VW brand in the form of an electric van (which also places "The Sound of Silence" on a different, ecofriendly cultural register). Thus, the ad's narrative follows the redemptive path of "inauthentic" (VW's Dieselgate) to "not inauthentic" (VW's 1960's countercultural heyday) to "authentic" (signifying that VW has rekindled its socially conscious roots).

Our discussion of the three authenticating strategies used by Slow Food consumers and entrepreneurs has emphasized their function as a defensive means to disavow or negate the disauthenticating associations that flow along the contradictory of deception path. However, these same strategies also promote affirmative meanings and associations that operate along the contradictory of redemption path. For example, the perfective strategy does more than negate the disauthenticating association with commercialism. It also magnifies authenticating differences to fast food or industrialized food production and thereby encourages consumers to interpret Slow Food enterprises in a manner compatible with the contradictory of redemption path (even though they may be aware of some disauthenticating associations). This redemptive associative chain takes the following form: Slow Food entrepreneurs are suspected to be "inauthentic" due to their commercial motivations → Slow Food entrepreneurs are seen as being "not inauthentic" because their deep commitment to artisan ideals and noncommercial values differentiates them from conventional fast-food establishments and industrialized modes of commercial food production → the signification of "not inauthentic" supports a broader conclusion that the Slow Food entrepreneur is an authentic actor.

Given their shared ideological affinities, the authenticating strategies used by Slow Food advocates should also have a high degree of applicability to brands espousing conscious capitalist goals and ideals. Of the three authenticating strategies, we find numerous examples of conscious capitalist brands that have enacted some version of the perfective strategy, which aims to negate associations with commercial opportunism and foster interpretations compatible with the contradictory of redemption path. While less commonplace, we can also find branding campaigns that align with the reflexive and humanistic rebel strategies. In the following discussion, we use these various exemplars to illustrate how these authenticating strategies can be implemented by conscious capitalist brands and the authenticity contradictions they potentially redress.

Brands using the perfective strategy engage in unconventional actions that demonstrate a deep commitment to activist causes that supersede profit motives. Over the years, Patagonia has made frequent use of this authenticating strategy to signal that proenvironmental values were central to its corporate mission, even when such acts could mean sacrificing sales, such as its iconic "Don't Buy" promotion ([49]) or their "Give a Damn" holiday messaging ([72]). REI has also enacted a perfective strategy in its #OptOutside campaign, whereby the retailer closes its stores on Black Friday and encourages consumers to engage in a range of proenvironmental, outdoor activities. Like Patagonia, REI's campaign builds on (and authenticates) the brand's history of supporting environmental causes and promoting a heightened concern for habitat protection and environmental conservation. Last but not least, Clif Bar illustrated a fairly novel implementation of the perfective strategy when its founder and chief executive officer, Gary Erickson, published an "advertorial" in the New York Times, offering to donate ten tons of organic ingredients to his main competitor Kind Bars. This advertorial further promised to share his company's knowledge about organic sourcing and production so that the two companies could collectively "lay the foundation for a healthier, more just and sustainable food system" ([26]).

Owing to their status as commercial enterprises, whose existence depends on profitability, the perfective strategies of conscious capitalist brands can always be reframed as yet another kind of commercial deception. However, such brands can lessen the cultural viability of such recursive challenges by further signaling that their passionate commitment to the supported causes takes precedence over profit motives. Though addressing a different context, [20] offer evidence that supports this strategic approach. They find that customers attribute the quality of authenticity to third-place establishments (e.g., cafes, coffee shops, restaurants) when they believe the respective proprietors are aiming to create meaningful social connections rather than merely trying to make a profit. As they write, "The authenticity perceived in treasured commercial places is based on exchanges that go beyond mere commercial aspects.... Although being business operators, proprietors invite the consumer to engage in activities that are not undertaken purely for profit" ([20], p. 913).

Accordingly, we propose that conscious capitalist brands are more likely to be perceived as authentic when they provide tangible means for consumers to participate in their social change mission but do so in ways that are not dependent on purchases. From this standpoint, The Body Shop's repositioning of its stores as activist hubs ([77])—where consumers can listen to speakers discuss environmental and social justice issues, sign petitions, and join activist organizations—is an enactment of the perfective strategy and a culturally viable means to reestablish the authenticity of its conscious capitalist branding claims.

This strategy aims to negate the charge that a market actor is evincing a "holier than thou" stance for actions that are either hypocritical (e.g., "do as I say, not as I do") or overstate the positive impact of the self-proclaimed act of conscious capitalist rectitude. For conscious capitalist brands, this authenticating logic most readily translates into a reformist agenda. As one prominent example, Chipotle's "Back to the Start" campaign (circa 2012) rallied a diverse assemblage of activist groups that shared a commitment to transforming the corporate-controlled system of food production and who saw the fast-food sector as exemplifying its presumed ills (see [48]). The two-minute short film, which ran across multiple media platforms, shows an increasingly disenchanted farmer witnessing the steady industrialization of his enterprise, replete with enclosed animals, the heavy use of antibiotics, and food being transformed into nondescript goo-like substances. Against the backdrop of Willie Nelson's plaintive version of Coldplay's "The Scientist," the farmer triumphantly decides to go "back to the start" by raising free-range animals, using traditional farming techniques, and selling his preindustrial goods to Chipotle.

Some relevant insights into this campaign and its authenticating effects can be gleaned from a Fast Company interview with Jesse Coulter, co–chief creative officer of Creative Artists Agency Marketing, which worked with Chipotle's management team in developing this campaign:

We were tasked to find new ways to tell Chipotle's Food with Integrity story.... The first issue Chipotle wanted to address was industrial farming.... Chipotle shared many stories of family farmers who have turned their farms into factory farms and have subsequently grown to regret it.... It was provocative because it took a stab at Big Agriculture. Chipotle is a bold company, who has the courage to really stand up for what they believe in.... At the end of the film, a title card appears letting people know that they can download the song on iTunes, and the proceeds benefit the Chipotle Cultivate Foundation, which is dedicated to creating a sustainable, healthy, and equitable food future. People responded and the song reached number one on the iTunes Country chart. ([15])

When interpreted through the lens of the reflexive strategy, Chipotle's "bold" move was to accept and amplify activist groups' criticisms of the fast-food industry's sourcing and production practices, rather than attempting to deny, rationalize, or obscure these problems through conventional images of consumers enjoying their fast-food meals. The campaign thereby draws an ideological distinction between Chipotle (as a reformist enterprise returning to more humane and sustainable agricultural practices) and the broader fast-food industry that is portrayed as having debased the time-honored practice of farming in the name of speed, efficiency, and cost reduction. In this way, Chipotle aligned itself not with the interests of the fast-food industry at large, but with activist groups who are seeking to reform the broader system of industrialized food production. Chipotle further reinforced the credibility of their reflexive strategy by investing additional resources to support the broader cause (i.e., creating a synergy between the reflexive and the perfective strategy).

This strategy promotes the brand as a means for reconstituting meaningful social connections and breaking down societal boundaries that artificially separate people. To avoid being just another nostalgic marketing ode, this strategy should take a critical stance toward selected status quo consumption and marketing practices. The intended message is that the conscious capitalist brand is enabling consumers to resist or escape the dehumanizing and/or isolating influences of materialism, status consciousness, and upward-ratcheting lifestyle competitions.

IKEA has run numerous campaigns that align with the humanistic rebel strategy. These campaigns embed its conscious capitalist commitments to sustainability and support of social justice issues, such as gender equity and LGBTQ rights, in a home-as-haven brand narrative. In these ads, the IKEA-furnished home represents a therapeutic space where people can, at least temporarily, unplug from the stresses and distractions of the "networked life" ([88], p. 17) and experience meaningful human connections and emotional fulfillment.

More than just a haven, however, IKEA often portrays the home as an active force that keeps at bay the outside forces that would interfere with the pleasures of slow living. In an ad titled "Home Is a Haven," we see a father and daughter running to their house during a rainstorm. As they approach the front door, the child's teddy bears spring to life as human-sized entities (whose muscular physiques resemble bouncers at a club). The bears rearrange the house into an open play area and protect the dad from intrusive calls and other outside distractions. We watch as father and daughter play dress-up and numerous other games, eventually falling asleep after their fully engaged bonding time (https://www.youtube.com/watch?v=wGgcYNlH02g).

IKEA's "Let's Relax" commercial presents a pointedly critical take on the performative, competitive affectations of Instagram micro-influencers, for whom everyday social activities are treated as an instrumental means to garner likes and followers. In the ad, we first observe an eighteenth-century family about to begin formal dinner in a very well-appointed dining room. Suddenly, the father halts the proceedings so that an artist can paint a portrait of the meal, which is immediately transported across the town in a horse-drawn carriage so that affirmative thumbs up gestures from the populace can be tabulated. The scene then suddenly shifts to a modern-day kitchen table, where the same father meticulously photographs the family meal, while his wife and children begrudgingly wait for this documenting ritual to end. The dad sheepishly retires his camera, and the family begins their more enjoyable and authentic social interactions, all framed by the closing caption: "Relax: It's a meal, not a competition" (https://www.youtube.com/watch?v=2BXRGzjo1%5fQ).

Drawing on our semiotic framework, we anticipate that conscious capitalist brands would gain the most authenticating benefit from the humanistic rebel strategy when they present their brands as ideological allies ([48]; [49]) of consumers who are sensitized to the psychological and social costs of careerism, exclusionary status hierarchies, and the calculated practices of social media self-presentations. In this way, the humanistic rebel strategy undercuts the elitist critique by suggesting that a conscious capitalist brand enables consumers to tap into more basic and rewarding emotional and sensory experiences. It further emphasizes that helping people, from all walks of life, feel genuinely connected to each other is an important and accessible way to make the world a better place.

Drawing from structural semiotics ([40]), we have developed a conceptual framework that can be used to analyze the cultural contradictions of authenticity, as they emerge in a given market context, and then to identify strategies for combatting their disauthenticating associations. Our analytic approach recognizes that perceptions of authenticity are constructed and contested in a dynamic cultural system. When negotiating such dynamism, marketing managers need to identify strategically significant patterns in the flux of cultural change and to adroitly react to cultural flash points, competitive shifts, and other exogenous shocks that could undermine the credibility of their existing authenticity claims. Whether undertaken in the context of conscious capitalist brands, status-marketing luxury goods, price-driven big-box retailers, or sharing-economy enterprises such as Uber or Airbnb, marketing managers can use our semiotic approach to more effectively negotiate the sociocultural complexity inherent to the process of authenticating their strategic assets.

1 Rob Kozinets

2 The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

3 The author(s) received no financial support for the research, authorship, and/or publication of this article.

4 For purposes of expositional clarity, our analysis will not explicitly highlight these two paths of contradictory relations. However, they are implicit to the operation of the contrariety and complementarity relations and, therefore, are part and parcel of what we more generally characterize as the contradictions of authenticity. In our closing discussion, we address the implications that the deception and redemption paths hold for marketing management.

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By Craig J. Thompson and Ankita Kumar

Reported by Author; Author

Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The "Word-of-Machine" Effect 

Rapid development and adoption of AI, machine learning, and natural language processing applications challenge managers and policy makers to harness these transformative technologies. In this context, the authors provide evidence of a novel "word-of-machine" effect, the phenomenon by which utilitarian/hedonic attribute trade-offs determine preference for, or resistance to, AI-based recommendations compared with traditional word of mouth, or human-based recommendations. The word-of-machine effect stems from a lay belief that AI recommenders are more competent than human recommenders in the utilitarian realm and less competent than human recommenders in the hedonic realm. As a consequence, importance or salience of utilitarian attributes determine preference for AI recommenders over human ones, and importance or salience of hedonic attributes determine resistance to AI recommenders over human ones (Studies 1–4). The word-of machine effect is robust to attribute complexity, number of options considered, and transaction costs. The word-of-machine effect reverses for utilitarian goals if a recommendation needs matching to a person's unique preferences (Study 5) and is eliminated in the case of human–AI hybrid decision making (i.e., augmented rather than artificial intelligence; Study 6). An intervention based on the consider-the-opposite protocol attenuates the word-of-machine effect (Studies 7a–b).

Keywords: algorithms; artificial intelligence; augmented intelligence; hedonic and utilitarian consumption; recommendations; technology

Recommendations driven by artificial intelligence (AI) are pervasive in today's marketplace. Ten years ago, Amazon introduced its innovative item-based collaborative filtering algorithm, which generates recommendations by scanning through a person's past purchased or rated items and pairing them to similar items. Since then, more and more companies are leveraging advances in AI, machine learning, and natural language processing capabilities to provide relevant and in-the-moment recommendations. For example, Netflix and Spotify use AI and deep learning to monitor a user's choices and provide recommendations of movies or music. Beauty brands such as Proven, Curology, and Function of Beauty use AI to make recommendations about skincare, haircare, and makeup. Real estate services such as OJO Labs, REX Real Estate, and Roof.ai have replaced human real estate agents with chatbots powered by AI. AI-driven recommendations are also pervading the public sector. For example, the New York City Department of Social Services uses AI to give citizens recommendations about disability benefits, food assistance, and health insurance.

In response to the proliferation of AI-enabled recommendations and building on long-standing research on actuarial judgments ([12]; [17]; [30]), recent marketing research has focused on whether consumers will be receptive to algorithmic advice in various domains ([ 9]; [14]; [24]; [26]; [27]). However, no prior empirical investigation has systematically explored if hedonic/utilitarian trade-offs in decision making determine preference for, or resistance to, AI-based (vs. human-based) recommendations.

We focus our investigation on hedonic/utilitarian attribute trade-offs because of their influence on both consumer choice and attitudes ([ 6]; [11]). Specifically, we examine when and why hedonic/utilitarian attribute trade-offs in decision making influence whether people prefer or resist AI recommenders. This question is of pivotal importance for managers operating in both the private and public sectors who are looking to harness the potential of AI-driven recommendations.

Across nine studies and using a broad array of both attitudinal and behavioral measures, we provide evidence of a "word-of-machine" effect. We define "word of machine" as the phenomenon by which hedonic/utilitarian attribute trade-offs determine preference for, or resistance to, AI-based recommendations compared with traditional word of mouth, or human-based recommendations. We suggest that the word-of-machine effect stems from a lay belief about differential competence perceptions regarding AI and human recommenders. Specifically, we show that people believe AI recommenders are more competent than human recommenders to assess utilitarian attribute value and generate utilitarian-focused recommendations. By contrast, people believe that AI recommenders are less competent than human recommenders to assess hedonic attribute value and generate hedonic-focused recommendations. As a consequence, and as compared with human recommenders, individuals are more (less) likely to choose AI recommenders when utilitarian (hedonic) attributes are important or salient, such as when a utilitarian (hedonic) goal is activated.

Our research is both theoretically novel and substantively impactful. A first set of theoretical contributions relates to research on the psychology of automation and on human–technology interaction ([12]; [17]; [30]). The pervasiveness of AI-driven recommendations has led to a burgeoning body of research examining whether consumers are receptive to the advice of algorithms, statistical models, and artificial intelligence ([14]; [24]; [27]). With respect to this literature, we make three novel contributions. First, we extend it by addressing the previously unexplored question of when and why hedonic/utilitarian trade-offs in decision making influence preference for or resistance to AI recommenders. Second, we show under what circumstances AI-driven recommendations are preferred to, and therefore more effective, than human ones: when utilitarian attributes are relatively more important or salient than hedonic ones. These results are especially noteworthy, as most research in this area has documented a robust and generalized resistance to algorithmic advice (for exceptions, see [ 9]; [15]; [26]). Third, we explore under what circumstances consumers will be amenable to AI recommenders in the context of human–AI partnerships: when AI supports rather than replaces a human. These results are also novel as researchers have just begun devising AI systems capable of deciding when to defer (vs. not defer) to a human ([18]), and empirical investigations are yet to examine if consumers will embrace such hybrid human–AI decision making.

Our research makes a second theoretical contribution to the literature on hedonic and utilitarian consumption ([ 1]; [22]; [31]; [44]). Prior research in this area has examined how the evaluation of hedonic and utilitarian products depends on characteristics of the task, locus of choice, and justifiability of choice (e.g., [ 5]; [ 8]; [34]). However, research in this area has not addressed the question of whether shifts in hedonic/utilitarian trade-offs in decision making determine preference for the source of a recommendation (e.g., an AI vs. a human recommender). Recent developments of AI have brought this question to the fore, making it of critical importance for companies seeking to leverage the potential of AI-driven recommendations.

From a managerial perspective, our results are useful for companies in both the private and public sectors that are looking to leverage AI recommenders to better reach their customers. As we investigate when consumers prefer AI over human recommenders, our findings are useful for companies debating if and how to effectively leverage AI-based recommendation systems. Our findings have implications for a host of marketing decisions. For instance, our results indicate that a shift away from hedonic attributes and toward utilitarian attributes leads to consumers preferring AI recommenders. Accordingly, AI recommenders may be more aligned with functional positioning strategies than experiential ones. In addition, emphasizing utilitarian benefits may be relatively more impactful with an AI-based system than emphasizing hedonic benefits. Taken together, our research and findings provide actionable insights for managers looking for ways to leverage AI to orchestrate consumer journeys so as to successfully move customers through the funnel, increase the likelihood of successful transactions, and, overall, optimize the customer experience at each phase of the journey.

Although consumption involves both hedonic and utilitarian considerations, consumers tend to view products as either predominantly hedonic or utilitarian (for a review, see [23]). Hedonic consumption is primarily affectively driven, based on sensory or experiential pleasure, reflects affective benefits, and is assessed on the basis of the degree to which a product is rewarding in itself ([ 8]; [11]; [21]). Utilitarian consumption is instead more cognitively driven, based on functional and instrumental goals, reflects functional benefits, and is assessed on the basis of the degree to which a product is a means to an end ([ 8]; [11]; [21]).

Prior research on hedonic/utilitarian consumption has focused on the effect of characteristics of the task on product judgments. For instance, choice tasks tend to favor utilitarian options, whereas rating tasks tend to favor hedonic options ([ 5]; [34]), and forfeiture increases the relative salience of hedonic attributes compared to acquisition ([13]). Justifiability leads people to assign greater weight to utilitarian (vs. hedonic) options ([34]), and hedonic (vs. utilitarian) choices are associated with greater perceived personal causality ([ 8]).

Although spanning over a decade, research on hedonic/utilitarian consumption has not yet addressed the question of whether hedonic and utilitarian trade-offs influence preference for the source of a recommendation (AI vs. human). This question has come to the fore given its importance for managers looking to leverage the potential of algorithmic recommendations. We discuss prior research on algorithmic recommendations in the next section.

Ever since seminal work on statistical and actuarial predictive models was published ([12]; [17]; [30]), a large body of research has documented how statistical/actuarial models outperform clinical/human judgments in predicting a host of events, such as students' and employees' performance ([12]) and market demand ([37]). Despite the superior accuracy of algorithmic models, people tend to eschew them. With only a few exceptions ([ 9]; [15]; [26]), most of the extant literature has shown that people resist the advice of a statistical algorithm. For instance, recent research in the medical domain has shown that consumers may be more reluctant to utilize medical care delivered by AI providers than by comparable human providers ([27]; [28]). Corporate settings show similar patterns, with recruiters ([19]) and auditors ([ 7]) trusting their judgment and predictions more than algorithms.

There are numerous reasons why people resist algorithmic recommendations. People (erroneously) believe that algorithms are unable to learn and improve ([12], [19]) and therefore lose confidence in algorithms when they see them err ([14]). People also believe that algorithms assume the world to be orderly, rigid, and stable and therefore cannot take into consideration uncertainty ([17]) and a person's uniqueness ([27]). Resistance to algorithmic advice may also be borne out of generalized concerns, such as people's fear of being reduced to "mere numbers" ([12]) and mistrust of algorithms' lack of empathy ([17]).

We extend this literature and show circumstances under which people prefer (and not just resist) algorithmic recommendations. Specifically, we examine how and why hedonic/utilitarian trade-offs determine preference for, or resistance to, AI recommenders, as articulated in the next section.

We hypothesize a word-of-machine effect, whereby hedonic and utilitarian trade-offs determine preference for or resistance to AI recommenders compared to human ones. We suggest that the word-of-machine effect stems from consumers' differing competence perceptions of AI and human recommenders in assessing attribute value and generating recommendations. Specifically, we suggest that people believe AI recommenders to be more (less) competent to assess utilitarian (hedonic) attribute value and generate utilitarian-focused (hedonic-focused) recommendations than human recommenders.

These predictions rest on the assumption that people believe hedonic and utilitarian attribute value assessment to require different evaluation competences. Hedonic value assessment should map onto criteria on the basis of experiential, emotional, and sensory evaluative dimensions. By contrast, utilitarian value assessment should map onto criteria on the basis of factual, rational, and logical evaluative dimensions. This assumption is rooted in the very definition of hedonic and utilitarian value. Hedonic value is conceptualized as reflecting experiential affect associated with a product, sensory enjoyment, and emotions ([ 4]; [20]). Indeed, hedonic consumption tends to be affectively rich and emotionally driven ([ 8]). By contrast, utilitarian value is conceptualized as reflecting instrumentality, functionality, nonsensory attributes, and rationality ([ 4]; [20]). Overall, utilitarian consumption is cognitively driven ([ 8]).

How do different types of recommenders (AI vs. human) then fare with respect to assessing hedonic and utilitarian attribute value? We suggest that people believe AI recommenders are more competent to assess utilitarian attribute value than human recommenders and less competent to assess hedonic attribute value than human recommenders. We attribute this lay belief to differing associations people have about how AI (vs. human) recommenders process and evaluate information. Lay beliefs are developed either directly through personal experience ([36]) or indirectly from the environment ([32]). Throughout childhood we learn firsthand that, as humans, we are able to perceive and connect with the outside world through our affective experiences. By contrast, we learn that AI, computers, and robots are rational and logical, and lack the ability to have affective, experiential interactions with the world. These associations are reflected in idioms such as "thinking like a robot," which refers to thinking logically without taking into consideration more "human" aspects of a situation such as sensations and emotions. Thus, whereas AI and computers are associated with rationality and logic, humans are associated with emotions and experiential abilities. These associations are also echoed in books, songs, and movies. For example, in the Star Trek universe, the artificially intelligent form of life named Data has superior intellective abilities but is unable to experience emotions. Popular movies like Her, Ex Machina, and Terminator further reinforce these associations.

Accordingly, we suggest that people believe AI recommenders are more competent than human recommenders when assessing information because they use criteria that rely relatively more on facts, rationality, logic, and, overall, cognitive evaluative dimensions. By contrast, we propose that people believe human recommenders are more competent than AI recommenders when assessing information because they use criteria that rely relatively more on sensory experience, emotions, intuition, and, overall, affective evaluative dimensions.

Because people perceive AI and humans to have different competency levels when assessing information, and because assessment of utilitarian and hedonic attribute value underscore different evaluative foci, it follows that people perceive AI and humans to have different competency levels with respect to assessing utilitarian and hedonic attributes. This lay belief about competence perceptions forms the basis for the proposed word-of-machine effect. In summary, we predict that if utilitarian (hedonic) attributes are more important or salient, such as when a utilitarian (hedonic) goal is activated, people will be more (less) likely to choose AI recommenders than human recommenders.

A final note warrants mention. As competence perceptions driving the word-of-machine effect are based on a lay belief, they are embedded in the cultural context. That is, humans are not necessarily less competent than AI at assessing and evaluating utilitarian attributes. Vice versa, AI is not necessarily less competent than humans at assessing and evaluating hedonic attributes. Indeed, AI selects flower arrangements for 1-800-Flowers and creates new flavors for food companies such as McCormick, Starbucks, and Coca-Cola ([41]).

Studies 1a–b focus on product choice in field settings and show the main word-of-machine effect: that AI (human) recommenders lead to greater choice likelihood when a utilitarian (hedonic) goal is activated. Study 2 shows different perceptions that result from the two recommendation sources: AI (human) recommenders lead to higher evaluation of utilitarian (hedonic) attributes upon consumption. Study 3 shows that when a utilitarian (hedonic) attribute is considered important, consumers prefer AI (human) recommenders. Study 4 uses an analysis of mediation to corroborate the role of competence perceptions in explaining the word-of-machine effect while ruling out attribute complexity as alternative explanation. Studies 5–7 explore the scope of the word-of-machine effect by identifying boundary conditions. Study 5 shows that the effect is reversed for utilitarian goals when the recommendation needs to match to a person's unique preferences, a type of task people view AI as unfit to do. Study 6 shows that the effect is eliminated when AI is framed as "augmented" intelligence rather than artificial intelligence, that is, when AI enhances and supports a person rather than replacing them. Finally, Studies 7a–b test an intervention using the consider-the-opposite protocol to moderate the word-of-machine effect.

Studies 1a–b focus on the word-of-machine effect on actual product choice in field settings as a function of an activated utilitarian or hedonic goal. We first activated either a utilitarian or a hedonic goal and then, in an incentive-compatible setting, measured choice as a function of recommender.

Two hundred passersby in a city in northeast United States participated in Study 1a on a voluntary basis. We handed willing passersby a leaflet explaining that we were conducting a blind test for products in the haircare industry and, specifically, for hair masks—a leave-in treatment for hair and scalp. Passersby read that for the purpose of the market test, we wanted them to select one of two hair mask samples solely on the basis of the instructions in the leaflet. These instructions activated, in a two-cell between-subjects design, either a hedonic or a utilitarian goal:

[Hedonic] For the purpose of this blind test, it is very important that you set aside all thoughts you might already have about hair masks. Instead, we would like you to focus only on the following. Imagine that you have a "hedonic" goal. We would like you to imagine that the only things that you care about in a hair mask are hedonic characteristics, like how indulgent it is to use, its scent, and the spa-like vibe it gives you. When you make the next choice, imagine that there are no other things that are important for you in a hair mask.

[Utilitarian] For the purpose of this blind test, it is very important that you set aside all thoughts you might already have about hair masks. Instead, we would like you to focus only on the following. Imagine that you have a "utilitarian" goal. We would like you to imagine that the only things that you care about in a hair mask are utilitarian characteristics, like how practical it is to use, its objective performance, and the chemical composition. When you make the next choice, imagine that there are no other things that are important for you in a hair mask.

The leaflet further explained that there were two hair mask options from which they could choose. One option had been recommended by a person, and the other option had been recommended by an algorithm. The leaflet specified that the person and the algorithm had the same haircare expertise and that the pots of hair masks, available for pickup on a desk, all contained the same amount of fluid ounces. The pots were identical except for a marking of "P" if selected by a person or "A" if selected by an algorithm (stimuli in Web Appendix A). The key dependent variable was whether passersby chose the product selected by the person or by the algorithm.

To assess product choice, we compared the proportion of people who chose the product recommended by the algorithm with the proportion of people who chose the product recommended by the person depending on the activated goal (utilitarian vs. hedonic). The two proportions differed significantly (χ²( 1, N = 200) = 12.60, p =.001). As predicted, when a utilitarian goal was activated, more people chose the product recommended by the algorithm (67%) than by the person (33%; z = 4.81, p <.001). When a hedonic goal was activated, more people chose the product recommended by the person (58%) than by the algorithm (42%; z = 2.26, p =.024).

Study 1b was a field study conducted over four consecutive days in Cortina, a resort town in northeast Italy. We selected this town because in 2026 it will host the Olympic games and is likely to experience a boom in its real estate market, which is the domain of the study. We secured the use of a centrally located bar and set up the study as follows. We placed an ad (translated to Italian) promoting a local real estate agency at the bar entrance. The ad headline reminded people of the opportunity to make fruitful real estate investments due to the upcoming Olympic games. In a two-cell, between-subjects design, we alternated the text in the ad to focus people on a hedonic or utilitarian goal:

[Hedonic] With the Olympic games coming up, it is really important that you look for a real estate investment that is fun, enjoyable, and speaks to your emotions. You want a place that pleases your senses considering all the changes that will affect [name of town] in the next few years.

[Utilitarian] With the Olympic games coming up, it is really important that you look for a real estate investment that is functional, useful, and speaks to your rationality. You want a place that is practical considering all the changes that will affect [name of town] in the next few years.

At the bottom of the ad there were two envelopes described as containing a curated selection of available properties in Cortina that could fit with the opportunity in the ad (i.e., one of the activated goals). One property selection had been (ostensibly) curated by a person (the respective envelope read: "one of [name of agency]'s agents has selected these properties") and the other by an algorithm (the respective envelope read: "[name of agency]'s proprietary algorithm has selected these properties"). The ad invited people to pick up only one of the two envelopes given the limited quantity of promotional materials (stimuli in Web Appendix B). The key dependent variable was whether people chose the selection made by the agent or by the algorithm. A waiter ensured that participants took only one of the two envelopes, and we excluded two participants who picked up two (final N = 229).

We compared the proportion of people who chose the selection made by the algorithm with the proportion of people who chose the selection made by the agent depending on the activated goal (utilitarian vs. hedonic). The two proportions differed significantly (χ²( 1, N = 229) = 29.33, p <.001). When the goal was utilitarian, more people chose the selection made by the algorithm (59.8%) than by the agent (40.2%; z = 3.07, p =.002), whereas when the goal was hedonic, more people chose the selection made by the agent (75.7%) than by the algorithm (24.3%; z = 7.52, p <.001).

Together, Studies 1a–b show that when a utilitarian goal is activated, people are more likely to choose an AI recommender than a human recommender. When a hedonic goal is activated, people are less likely to choose an AI recommender than a human recommender.

Study 2 examines the word-of-machine effect upon consumption. As conceptual information such as expectations affects food consumption experiences (e.g., [ 2]; [42]), we predicted that the type of recommender would affect perceptions of hedonic and utilitarian attributes upon actual consumption of a product (a chocolate cake).

One hundred forty-four participants from a paid subject pool (open to students and nonstudents) at the University of Virginia completed this study (M_age = 27.5 years, SD = 9.5; 60.4% female). We told participants that we were testing chocolate cake recipes on behalf of a local bakery (stimuli in Web Appendix C). We told participants that the bakery had two options for chocolate cake recipes: one created using the ingredient selection of an AI chocolatier and one created using the ingredient selection of a human chocolatier. We specified that both the human and AI chocolatier had access to the same recipe database. We invited participants to look at the two chocolate cakes on top of a podium in a pop-up bakery/classroom desk. The two types of cake looked (and were) identical. We told participants that the two chocolate cakes, although based on different recipes, looked the same because the bakery did not want them to be influenced by the shape or the color. In a two-cell between-subjects design, we asked participants to consume either the chocolate cake whose recipe was selected by the human chocolatier or the one selected by the AI chocolatier. After consuming the cake, we measured hedonic/utilitarian attribute perceptions by asking participants to rate the cake on two hedonic items (indulgent taste and aromas; pleasantness to the senses [vision, touch, smell, etc.]) and two utilitarian items (beneficial chemical properties [antioxidants]; healthiness [micro/macro nutrients, etc.]) on seven-point scales anchored at 1 = "very low" and 7 = "very high." The order of hedonic and utilitarian items was randomized.

A one-way analysis of variance (ANOVA) on the average of the two hedonic items (r =.87, p <.001) revealed that, upon consumption, participants rated the chocolate cake as having lower hedonic value when based on the recommendation of an AI chocolatier than a human one (M_AI = 4.57, SD = 1.38; M_H = 6.17, SD = 1.03; F( 1, 142) = 61.33, p <.001).

A one-way ANOVA on the index of the two utilitarian items (r =.84, p <.001) revealed that, upon consumption, participants rated the chocolate cake as having higher utilitarian value when based on the recommendation of an AI chocolatier than a human one (M_AI = 5.48, SD = 1.21; M_H = 5.02, SD = 1.35; F( 1, 142) = 61.33, p =.034).

Thus, Study 2 shows that the word-of-machine effect extends to actual consumption and that the type of recommender influences people's perceptions of hedonic/utilitarian trade-offs. AI recommenders led participants to perceive greater utilitarian attribute value and lower hedonic attribute value compared to human recommenders.

Study 3 further tests the word-of-machine effect. Instead of activating hedonic/utilitarian goals as in Studies 1a–b, we measured the importance given to hedonic/utilitarian attributes with respect to a specific product category (winter coats). Then, we assessed relative preference for a human or an AI recommender. We expected people to prefer AI to human recommenders when utilitarian attributes were more important to them, and to prefer human over AI recommenders when hedonic attributes were more important to them. We benchmarked these hypotheses with a condition in which people chose between two human recommenders, wherein we expected recommender preference to be uncorrelated with importance assigned to hedonic/utilitarian attributes.

Three hundred three respondents (M_age = 38.0 years, SD = 11.1; 49.5% female) recruited on Amazon Mechanical Turk participated in exchange for monetary compensation. Participants imagined that they were planning to purchase a new winter coat (as it was the winter season) and were looking for recommendations. Participants read that winter coats have functional/utilitarian aspects ("Winter coats have functional or utilitarian aspects, such as insulating power, breathability, and the degree to which the coat is rain and wind proof") and sensory/hedonic aspects ("Winter coats have sensory or hedonic aspects, such as the color and other aesthetics, the way the fabric feels to the touch, and the degree to which the coat fits well"). Then, to measure the importance of hedonic/utilitarian attributes, participants rated the extent to which, in general, they cared about sensory/hedonic and functional/utilitarian aspects in winter coats (1 = "mostly care about functional/utilitarian aspects," and 7 = "mostly care about sensory/hedonic aspects").

Participants then read that to get recommendations about winter coats, they could rely on one of two shopping assistants, X or Y. We specified that both assistants had access to the same type and size of database, would charge the same fees, would generate recommendations autonomously, and were trained to serve users well and to the best of their capacity. To control for the possibility that different recommenders would be associated with different service quality perceptions, we also specified that the two shopping assistants had the same rating of 4.9/5.0 stars provided by 687 consumers that had used their services in the past. To manipulate choice set, half of the participants chose between two human shopping assistants (both X and Y were people and were described as two different sales associates at that particular retailer), and the other half chose between a human assistant, X, and an AI assistant, Y. Thus, whereas X was always human, Y was either human or AI depending on the condition. Finally, participants indicated their preference for one of the assistants (1 = "definitely shopping assistant X," 4 = "indifferent," and 7 = "definitely shopping assistant Y").

We regressed recommender preference on choice set (human–human vs. human–AI), hedonic/utilitarian attribute importance, and their interaction. This analysis revealed significant main effects of choice set (b =.85, t(299) = 5.49, p <.001) and hedonic/utilitarian attribute importance (b =.32, t(299) = 7.46, p <.001), as well as a significant two-way interaction (b = −.29, t(299) = −6.91, p <.001). As hedonic/utilitarian attribute importance was continuous, we explored the interaction using the Johnson–Neyman floodlight technique ([39]), which revealed a significant effect of recommender preference in human–AI choice set for levels of hedonic/utilitarian attribute importance lower than 2.35 (b_JN =.15, SE =.08, p =.050) and higher than 3.36 (b_JN = −.14, SE =.07, p =.050). That is, the more participants cared about utilitarian attributes (values lower than 2.35 on the seven-point scale), the more they preferred an AI assistant over a human one. Conversely, the more participants cared about hedonic attributes (values higher than 3.36 on the seven-point scale), the more they preferred a human assistant over an AI one. As predicted, in the human–human choice set, which served as the control condition, participants were indifferent between the two assistants (M = 3.98, SD =.34) and recommender preference was uncorrelated with hedonic/utilitarian attribute importance (r =.116, p =.162; see Figure 1).

Graph: Figure 1. Results of Study 3: Preference for AI (human) recommenders when utilitarian (hedonic) attributes are more important.Notes: The y-axis represents preference for recommender measured on a seven-point scale anchored at 1 = "definitely shopping assistant X," and 7 = "definitely shopping assistant Y." The x-axis represents importance of hedonic/utilitarian attributes, measured on a seven-point scale anchored at 1 = "mostly care about functional/utilitarian aspects," and 7 = "mostly care about sensory/hedonic aspects." The shaded region represents area of significance.

These results provided correlational evidence that hedonic/utilitarian attribute importance predicts preference between human and AI recommenders. The next study utilizes an analysis of mediation to test competence perceptions as drivers of the word-of-machine effect.

Study 4 uses an analysis of mediation to measure competence perceptions as lay beliefs underlying the word-of-machine effect. In addition, this study tests attribute complexity as an alternative explanation: a belief that AI recommenders are better capable to process more complex attribute information than human recommenders. One could argue that utilitarian attributes seem more complex to evaluate than hedonic attributes. If this argument is accurate, preference for AI recommenders when utilitarian attributes are more salient could be explained by a lay belief about the recommender's ability, higher for AI recommenders, to deal with complexity.[ 7] We tested this alternative explanation by manipulating attribute complexity orthogonally to recommender type (human, AI) and activated goal (hedonic, utilitarian). We manipulated attribute complexity by way of number of product attributes, which is consistent with prior research ([25]; [40]).

Four hundred two participants (M_age = 38.5 years, SD = 12.6; 46% female) from Amazon Mechanical Turk participated in exchange for monetary compensation in a 2 (complexity: low, high) × 2 (goal: hedonic, utilitarian) × 2 (recommender: human, AI) between-subjects design.

Participants read about the beta testing of a new app created to give recommendations of chocolate varieties by relying on one of two sources: a human or an AI master chocolatier (i.e., a computer algorithm). We told participants that the human and AI recommenders relied on the same database of chocolate varieties and operated autonomously. The app had the same cost regardless of recommender. Participants saw screenshots of the app (Figure 2).

Graph: Figure 2. Stimuli of Study 4.

We specified that the ratings of the chocolate varieties in the data set were not based on personal experience but rather that they had been rated by consumers and manufacturers in terms of certain dimensions that varied by complexity condition. In the high complexity condition, we described the chocolate varieties as being rated on eight attributes, four of which were hedonic (sensory pleasure, taste, fun factor, and pairing combinations) and four of which were utilitarian (chemical profile, nutritional index, digestibility profile, and health factor). In the low complexity condition, we described the chocolate varieties as being rated on two attributes, one of which was hedonic (sensory pleasure) and one of which was utilitarian (chemical profile).We then activated either a hedonic or a utilitarian goal by asking participants to set aside all thoughts they might already have had about chocolate and instead imagine that they wanted a recommendation based only on ( 1) sensory pleasure, taste, fun factor, and pairing combinations (hedonic/high complexity); ( 2) sensory pleasure (hedonic/low complexity); ( 3) chemical profile, nutritional index, digestibility profile, and health factor (utilitarian/high complexity); or ( 4) chemical profile (utilitarian/low complexity). Finally, we manipulated recommender in a two-cell (recommender: human, AI) between-subjects design by telling participants that in the version of the app they were considering, it was either the human or the AI master chocolatier that would give them a recommendation.

As a behavioral dependent variable, we asked participants if they wanted to download the chocolate recommendation at the end of the survey (yes, no), specifying that payment would not be conditional on electing to download the recommendation (which is consistent with previous research; see [10]). We then measured the hypothesized mediator (competence perceptions) by asking participants to rate the extent to which they thought the human (AI) recommender ( 1) was competent to recommend the type of chocolate they were looking for and ( 2) could do a good job recommending the type of chocolate they were looking for (1 = "strongly disagree," and 7 = "strongly agree"; r =.89, p <.001).[ 8] At the very end of the survey, participants who elected to download the recommendation were automatically directed to a downloadable PDF document with information about the chocolate (a relatively more indulgent hazelnut-based chocolate called "gianduiotti" in the hedonic condition or a relatively healthier chocolate toasted at low temperature called "crudista" in the utilitarian condition).

We assessed behavior (i.e., the proportion of participants who decided to download vs. not download the recommendation) by using a logistic regression with complexity, goal, recommender, and their two-way and three-way interactions as independent variables (all contrast coded) and download (1 = yes, 0 = no) as dependent variable. We found no significant main effect of complexity (B = −.04, Wald =.09, 1 d.f., p =.77) or goal (B =.03, Wald =.06, 1 d.f., p =.81), and we found a marginally significant main effect of recommender (B =.25, Wald = 3.75, 1 d.f., p =.053). The three-way goal × recommender × complexity interaction was not significant (B = −.11, Wald =.80, 1 d.f., p =.37), ruling out the role of complexity. In terms of two-way interactions, complexity did not interact with goal (B = −.13, Wald = 1.04, 1 d.f., p =.31) nor with recommender (B = −.18, Wald = 1.99, 1 d.f., p =.16). Replicating previous results, the two-way goal × recommender interaction was significant (B =.75, Wald = 34.60, 1 d.f., p <.001). The AI recommender led to more downloads than the human recommender when the goal was utilitarian (M_AI = 82%, M_H = 63%; z = 3.10, p =.002) and fewer downloads when the goal was hedonic (M_AI = 52%, M_H = 88%; z = −5.63, p <.001).

A 2 × 2 × 2 ANOVA on competence perceptions revealed no significant main effect of complexity (F( 1, 394) = 1.24, p =.27) and significant main effects of goal (F( 1, 394) = 8.99, p =.003) and recommender (F( 1, 394) = 19.81, p <.001). The three-way complexity × goal × recommender interaction was not significant (F( 1, 394) =.64, p =.44), ruling out complexity. In terms of two-way interactions, complexity did not interact with goal (F( 1, 394) =.61, p =.44), nor with recommender (F( 1, 394) =.36, p =.55). Importantly, the two-way goal × recommender interaction was significant (F( 1, 394) = 57.63, p <.001). Planned contrasts revealed that participants perceived the AI recommender as more competent than the human recommender in the case of a utilitarian goal (M_AI = 5.92, SD_AI = 1.10; M_H = 5.50, SD_H = 1.38; F( 1, 394) = 4.90, p =.027) and less competent in the case of a hedonic goal (M_AI = 4.51, SD_AI = 1.77; M_H = 6.13, SD_H =.96; F( 1, 394) = 73.04, p <.001).

We ran a moderated mediation model using PROCESS Model 8 ( 5,000 resamples; Hayes 2018). In this model, the moderating effect of goal takes place before the mediator (competence perceptions). The interaction between recommender and goal was significant (95% CI =.38 to.64) in the path between the independent variable and the mediator but not in the path between the independent variable and the dependent variable (95% CI = −.08 to.63). As predicted, the indirect effect recommender → competence perceptions → download was significant but in the opposite direction conditionally on the moderator (hedonic: 95% CI = 1.19 to 2.40; utilitarian: 95% CI = −.88 to −.06).

These results provide evidence for the hypothesized role of competence perceptions as drivers of the word-of-machine effect. Participants rated AI recommenders as more (less) competent in the case of utilitarian (hedonic) goals. Differential competence perceptions explained higher choice likelihood for the AI's recommendation than the human's if a utilitarian goal had been activated and lower choice likelihood for the AI's recommendation than the human's if a hedonic goal had been activated. Furthermore, we did not find evidence that the word-of-machine effect was moderated by complexity. The next three studies tested the scope of the word-of-machine effect by identifying boundary conditions.

Study 5 explores a circumstance under which the word-of-machine effect might reverse: when consumers want a recommendation that matches their unique needs and preferences.[ 9] Matching a recommendation to one's preferences is valued and might even be expected ([16]). In this study, we tested the hypothesis that consumers view the task of matching a recommendation to one's unique preferences as being better performed by a person than by AI.[10] This argument is in line with recent research in the medical domain showing that consumers perceive AI as less able than a human physician to tailor a medical recommendation to their unique characteristics and circumstances ([27]). Thus, we expected people to choose AI recommenders at a lower rate and, conversely, choose human recommenders at a higher rate if matching to unique preferences was salient, even in the case of an activated utilitarian goal. In other words, if matching to unique preferences was salient, we expected people to prefer a human recommender for both hedonic and utilitarian goals. We tested this possibility by manipulating whether participants' desire to have a recommendation matched to their unique needs and preferences was salient and then measuring their choice of recommender.

Five hundred forty-five respondents (M_age = 39.0 years, SD = 12.9; 46.6% female) from Amazon Mechanical Turk participated in exchange for monetary compensation in a 2 (goal: hedonic, utilitarian) x 2 (matching: unique preferences, control) between-subjects design. Participants read information about the beta testing of a new smartphone app offered by a real estate service. The app would allow users to chat with a Realtor to find properties to buy or rent. Participants further read that there were two versions of this app. In one version of the app, users would interact with a human Realtor, and in the other version, users would interact with an AI Realtor (i.e., a computer algorithm). Participants saw screenshots of the app (Figure 3) and read about how the app would work: the users would indicate what attributes they were looking for in a property (square footage, number of rooms, budget) and the [Realtor/AI Realtor] would use [their/its] training and knowledge to make apartment recommendations. We specified that both the human and AI Realtors had access to the same number and type of property listings. We then activated either a hedonic or a utilitarian goal by asking participants to set aside all thoughts they might already have had about apartments and instead imagine that they wanted a recommendation based only on: ( 1) how trendy the neighborhood is, the apartment views, aesthetics (hedonic goal condition) or ( 2) distance to their workplace, proximity to public transport, functionality (utilitarian goal condition; based on [ 6]). Finally, to make unique preference matching salient, we told half of the participants that it was very important for them to get a recommendation that would be matched to their unique needs and personal preferences. Participants in the control condition were not focused on unique preference matching. As a dependent variable, we measured choice of recommender by asking participants if, given the circumstances described, they wanted to chat with the human or the AI Realtor.

Graph: Figure 3. Stimuli (top) and results (bottom) of Study 5: The word-of-machine effect is reversed for utilitarian goals if the recommendation needed to match participants' unique preferences.Notes: The y-axis represents the proportion of participants who chose to chat with the human versus AI realtor.

We assessed choice on the basis of the proportion of participants who decided to chat with the human versus AI Realtor by using a logistic regression with goal, matching, and their two-way interaction as independent variables (all contrast coded) and choice (0 = human, 1 = AI) as a dependent variable. We found significant effects of goal (B = 1.75, Wald = 95.70, 1 d.f., p <.000) and matching (B =.54, Wald = 24.30, 1 d.f., p <.000). More importantly, goal interacted with matching (B =.25, Wald = 5.33, 1 d.f., p =.021). Results in the control condition (when unique preference matching was not salient) replicated prior results: in the case of an activated utilitarian goal, a greater proportion of participants chose the AI Realtor (76.8%) over the human Realtor (23.2%; z = 8.91, p <.001), and when a hedonic goal was activated, a lower proportion of participants chose the AI (18.8%) over the human Realtor (81.2%; z = 10.35, p <.001). However, making unique preference matching salient reversed the word-of-machine effect in the case of an activated utilitarian goal: choice of the AI Realtor decreased to 40.3% (from 76.8% in the control; z = 6.17, p <.001). That is, making unique preference matching salient turned preference for the AI Realtor into resistance despite the activated utilitarian goal, with most participants choosing the human over the AI Realtor. In the case of an activated hedonic goal, making unique preference matching salient further strengthened participants' choice of the human Realtor, which increased to 88.5% from 81.2% in the control, although the effect was marginal, possibly due to a ceiling effect (z = 1.66, p =.097).

Overall, whereas the word-of-machine effect replicated in the control condition when unique preference matching was salient, participants preferred the human Realtor over the AI recommender both in the hedonic goal conditions (human = 88.5%, AI = 11.5%; z = 12.40, p <.001) and in the utilitarian goal conditions (human = 59.7%, AI = 40.3%; z = 3.24, p =.001; Figure 3), corroborating the notion that people view AI as unfit to perform the task of matching a recommendation to one's unique preferences.

These results show that preference matching is a boundary condition of the word-of-machine effect, which reversed in the case of a utilitarian goal when people had a salient goal to get recommendations matched to their unique preferences and needs. The next study tests another boundary condition.

Study 6 explores under what circumstances the word-of-machine effect is eliminated, and it tests the role of AI as boundary condition. Studies 1–5 tested cases in which the role of AI was to replace human recommenders. Study 6 explores the case in which AI is leveraged to assist and augment human intelligence. "Augmented intelligence" involves AI's assistive role in enhancing and amplifying human intelligence instead of replacing it ([ 3]). So far, we have showed that consumers resist AI recommenders when a hedonic goal is activated. In Study 6, we tested the hypothesis that consumers will be more receptive to AI recommenders, even in the case of a hedonic goal, if the AI recommender assists and amplifies a human recommender who retains the role of ultimate decision maker. In this case, we expected people to believe that the human decision maker would compensate for the AI's relative perceived incompetence in the hedonic realm. We expected the reverse effect in the case of a utilitarian goal. In other words, we expected that augmented intelligence—a human–AI hybrid decision making model— would help bolster AI to the level of humans for hedonic decision making and help bolster humans to the level of AI for utilitarian decision making. In addition, we added a control condition in Study 6 in which neither recommender was mentioned to serve as a baseline measure of participants' perceptions of hedonic and utilitarian attributes.

Four hundred four respondents (M_age = 40.2 years, SD = 12.5; 48.9% female) from Amazon Mechanical Turk participated in exchange for monetary compensation in a three-cell (recommender: human, artificial intelligence, augmented intelligence) between-subjects design. A fourth control condition contained no recommender manipulation and served as the baseline.

The stimuli and procedure were identical to those of Study 4. Participants read about the beta testing of a new app created to give recommendations of chocolate varieties by relying on one of two sources: a human or an AI master chocolatier. Participants read that human and AI recommenders relied on the same database, which comprised a large number of chocolate varieties that had been rated by consumers and manufacturers. Participants read that the app had the same cost regardless of the type of recommender it relied on. Finally, participants read that the app would suggest a curated selection of five chocolate bars.

We then manipulated recommender by randomly assigning participants to ( 1) a human condition, in which a human chocolatier would curate the chocolate section; ( 2) an artificial intelligence condition, in which an AI chocolatier (i.e., a computer algorithm) would curate the chocolate section; or ( 3) an augmented intelligence condition, in which the AI chocolatier would assist the human chocolatier in the curation of the chocolate selection. Specifically, participants read:

[Human condition] In the version of the app we are testing today, it is the human chocolatier that curates a selection of chocolate bars. This selection contains five chocolate bars selected by the human chocolatier. That is, it is a person who selects chocolate bars. This version of the app is technically called "human intelligence," because it uses what human intelligence can do.

[Artificial intelligence condition] In the version of the app we are testing today, it is the A.I. chocolatier that curates a selection of chocolate bars. This selection contains five chocolate bars selected by the A.I. chocolatier. That is, it is a computer algorithm that selects chocolate bars. This version of the app is technically called "artificial intelligence," because it uses a computer algorithm to substitute and replace what human intelligence can do.

[Augmented intelligence condition] In the version of the app we are testing today, it is the A.I. chocolatier that curates a selection of chocolate bars. This selection contains five chocolate bars selected by the A.I. chocolatier. That is, it is a computer algorithm that selects chocolate bars. The computer algorithm makes the initial selection and assists a human chocolatier, who will make the final decision about which chocolate bars to recommend. This version of the app is technically called "augmented intelligence," because it uses a computer algorithm to enhance and augment what human intelligence can do.

The control condition entailed no recommender manipulation; instead, it merely included a description of the app and no information about the source of the chocolate bar recommendation. As a dependent variable, we measured hedonic attribute perceptions with two items (indulgent taste and aromas; pleasantness to the senses [vision, touch, smell, etc.]) and utilitarian attribute perceptions with two items (beneficial chemical properties [antioxidants, etc.]; healthiness [micro/macro nutrients, etc.]), all on seven-point scales anchored at 1 = "very low," and 7 = "very high." The order of items was randomized.

The one-way ANOVA on the average of the two items measuring hedonic attribute perceptions (r =.79, p <.001) was significant (F( 1, 436) = 48.92, p <.001). In line with previous results, and replicating the word-of-machine effect, participants reported higher hedonic attribute perceptions when the recommender was human (M_H = 6.00; SD = 1.06) than when the recommender was AI (M_{artificial_intelligence} = 4.15, SD = 1.64; F( 1, 436) = 125.55, p <.001). However, when the AI recommender was augmenting human intelligence, the word-of-machine effect was eliminated: participants reported the same hedonic perceptions (M_{augmented_intelligence} = 5.74, SD = 1.11) as they did when the recommender was human (F( 1, 436) = 2.31, p =.129) and higher hedonic perceptions than when the recommender was AI alone (F( 1, 436) = 84.73, p <.001). Participants in the control condition reported lower hedonic perceptions (M_control = 5.62, SD = 1.09) than participants in the human condition (F( 1, 436) = 5.32, p =.022) and higher hedonic perceptions than participants in the AI condition (F( 1, 436) = 77.92, p <.001). Control condition and augmented intelligence condition did not differ (F( 1, 436) < 1, p =.49).

The one-way ANOVA on the average of the two items measuring utilitarian attribute perceptions (r =.75, p <.001) was significant (F( 1, 436) = 6.60, p <.001). In line with previous results, and replicating the word-of-machine effect, participants reported higher utilitarian attribute perceptions when the recommender was AI (M_{artificial_intelligence} = 5.24; SD = 1.41) than when the recommender was human (M_H = 4.75, SD = 1.57; F( 1, 436) = 6.40, p =.012). However, when the AI recommender was augmenting human intelligence, the word-of-machine effect was eliminated: participants reported the same utilitarian perceptions (M_{augmented_intelligence} = 5.44, SD = 1.32) as they did when the recommender was AI alone (F( 1, 436) =.99, p =.321) and higher utilitarian perceptions than when the recommender was human (F( 1, 436) = 11.87, p <.001). Participants in the control condition reported the same utilitarian perceptions (M_control = 4.70, SD = 1.56) as participants in the human condition (F( 1, 436) =.05, p =.820) and lower utilitarian perceptions than participants in both the AI (F( 1, 436) = 7.47, p =.007) and augmented intelligence conditions (F( 1, 436) = 13.22, p <.001; Figure 4).

Graph: Figure 4. Results of Study 6: The word-of-machine effect is eliminated in the case of augmented intelligence (human–AI hybrid decision making).Notes: The y-axis represents hedonic attribute perceptions and utilitarian attribute perceptions measured on seven-point scales anchored at 1 = "very low," and 7 = "very high." Error bars represent standard errors. The solid-line pairwise comparisons represent the word-of-machine effect. The dashed-line pairwise comparisons represent moderation by augmented intelligence: A human–AI hybrid decision making model bolsters AI to the level of humans for hedonic decision making, and humans to the level of AI for utilitarian decision making. Details of all pairwise comparisons are reported subsequently.Hedonic Attribute PerceptionsWord-of-machine effect: Human versus AI: F( 1, 436) = 125.55, p =.000Moderation by augmented intelligence (H + AI hybrid decision making bolsters AI to the level of humans for hedonic decision making): Human versus H + AI: F( 1, 436) = 2.31, p =.129AI versus H + AI: F( 1, 436) = 84.73, p =.000Control versus H: F( 1, 436) = 5.32, p =.022Control versus AI: F( 1, 436) = 77.92, p =.000Control versus H + AI: F( 1, 436) =.49, p =.486Utilitarian Attribute PerceptionsWord-of-machine effect: Human versus AI: F( 1, 436) = 6.40, p =.012Moderation by augmented intelligence (H + AI hybrid decision making bolsters H to the level of AI for utilitarian decision making): AI versus H + AI: F( 1, 436) =.99, p =.321H versus H + AI: F( 1, 436) = 11.87, p =.001Control versus H: F( 1, 436) =.05, p =.820Control versus AI: F( 1, 436) = 7.47, p =.007Control versus H + AI: F( 1, 436) = 13.22, p =.000

These results delineate the scope of the word-of-machine effect and show a circumstance under which the effect is eliminated. Even when a hedonic goal was activated, AI recommenders fared as well as human recommenders as long as they were in a hybrid decision-making model in partnership with a human.

Studies 7a and 7b test an intervention to attenuate the lay belief underlying the word-of-machine effect—that AI recommenders are less (more) competent than human recommenders in assessing hedonic (utilitarian) value. We used a protocol called "consider-the-opposite," in which people are prompted to consider the opposite of what they initially believe to be true and take into account evidence that is inconsistent with one's initial beliefs. This protocol has been effectively used to correct biased beliefs in judgment, such as the explanation bias ([29]), confirmatory hypothesis testing ([43]), anchoring ([33]) and halo effects in marketing claims ([35]). Study 7a tests this intervention following the original protocol (i.e., [33]), and Study 7b tests a protocol that is relatively easier to implement and scale by embedding the intervention in a real chatbot.

Three hundred sixty-eight respondents (M_age = 39.8 years, SD = 12.5; 49.2% female) from Amazon Mechanical Turk participated in exchange for monetary compensation in a 2 (recommender: human, AI) × 2 (intervention: consider the opposite, control) between-subjects design.

The stimuli and procedure were identical to those of Studies 4 and 6: participants read about a new app created to give chocolate recommendations by relying on either a human or an AI master chocolatier. We manipulated recommender between subjects by telling participants that, in the version of the app they were considering, it was either the human or the AI chocolatier that would suggest a curated selection of five chocolate bars. We also implemented the intervention between subjects by prompting half of the participants to "consider the opposite": consider the ways in which they could be wrong about what they expected the [human/AI] recommender to be good at (based on [33]):

Think for a moment about what you expect the [human/AI] chocolatier to be good at when selecting chocolate bars. Before you rate the chocolate selection, we would like you to consider the opposite. Can your expectations about what the human chocolatier is good at when selecting chocolates be wrong? Imagine that you were trying to be as unbiased as possible in evaluating this chocolate selection—consider yourself to be in the same role as a judge or juror. Could the [human/AI] chocolatier be good at the opposite of what you expect them to be good at? Please write down some ways in which you could be wrong in terms of your expectations about what the [human/AI] chocolatier is good at when selecting chocolates.

This prompt was absent for participants in the control condition. As a dependent variable, participants reported their perceptions of hedonic/utilitarian attributes of the curated selection of chocolate bars, measured on a seven-point scale ranging from 1 = "sensory pleasure (taste, aromas, etc.)" to 7 = "healthy chemical properties (antioxidants, micro/macro nutrients, etc.)." Thus, lower numbers indicated higher hedonic value.

A 2 × 2 ANOVA on hedonic/utilitarian attribute perceptions revealed no significant main effect of intervention (F( 1, 364) =.25, p =.62), a significant main effect of recommender (F( 1, 364) = 65.17, p <.001), and a significant two-way recommender × intervention interaction (F( 1, 364) = 12.11, p =.001). Planned contrasts revealed that the word-of-machine effect replicated both in the control and intervention conditions, with lower hedonic perceptions (or, conversely, higher utilitarian perceptions) for AI recommenders than human recommenders (control conditions: M_{AI_control} = 4.51, SD = 1.84, M_{H_control} = 2.49, SD = 1.48; F( 1, 364) = 81.48, p <.001; intervention conditions: M_{AI_intervention} = 3.99, SD = 1.78, M_{H_intervention} = 3.18, SD = 1.44; F( 1, 364) = 8.93, p =.003; higher numbers indicate higher utilitarian/lower hedonic perceptions). More importantly, the intervention attenuated the word-of-machine effect and led to participants perceiving the AI's recommendation as having higher hedonic value compared with the control condition (M_{AI_intervention} = 3.99, SD = 1.78, M_{AI_control} = 4.51, SD = 1.84; F( 1, 364) = 7.66, p =.006) and the human recommendation as having higher utilitarian value compared to the control condition (M_{H_intervention} = 3.18, SD = 1.44; M_{H_control} = 2.49, SD = 1.48, F( 1, 364) = 4.59, p =.033; higher numbers indicate higher utilitarian/lower hedonic perceptions; Figure 5).

Graph: Figure 5. Results of Study 7a: Prompting people to consider the opposite attenuated the word-of-machine effect.Notes: The y-axis represents perceived hedonic/utilitarian attribute value measured on a seven-point scale anchored at 1 = "sensory pleasure (taste, aromas, etc.), and 7 = "healthy chemical properties (antioxidants, micro/macro nutrients, etc.)"; therefore, higher numbers indicate higher utilitarian value/lower hedonic value. Error bars represent standard errors.

Thus, these results provide evidence for a potential intervention that alleviates initial beliefs about, and therefore resistance to, AI recommenders: prompting people to consider the opposite.

Study 7b builds on the original consider-the-opposite protocol and the results of Study 7a to test an intervention better suited for implementation and scalability in a real-world setting. To do so, we created a real chatbot that participants could interact with and that delivered the intervention.

Two hundred nighty-nine respondents (M_age = 40.4 years, SD = 12.6; 43.1% female) from Amazon Mechanical Turk participated in exchange for monetary compensation in a two-cell (intervention: consider the opposite, control) between-subjects design. Participants read about an app called "Cucina" that would rely on AI to give recipe recommendations. The app worked by giving users the chance to chat with the AI Chef and ask for recipe suggestions and recommendations. Participants further read that they could try out the AI Chef by chatting with it in a web browser window. We created a chatbot ad hoc for this experiment by embedding a JavaScript in the Qualtrics survey (Figure 6). The chatbot was programmed to first introduce itself: "Hello I am an A.I. Chef at Cucina! Thank you for trying out our app! What is your name?" Participants could then reply to the chatbot using a text box. We programmed the chatbot's next response to differ depending on the intervention condition:

[Intervention: consider the opposite] "Hi [participant's name]! I am here to suggest a recipe for you to try! Some people might think that an Artificial Intelligence Chef is not competent to give food suggestions...but this is a misjudgment. For a moment, set aside your expectations about me. When it comes to making food suggestions, could you consider the idea that I could be good at things you do not expect me to be good at? Okay, let's chat about food. How can I help you?"

Graph: Figure 6.Stimuli of Study 7b.

[Intervention: control] "Hi [participant's name]! I am here to suggest a recipe for you to try! Okay, let's chat about food. How can I help you?"

As a dependent variable, we measured hedonic/utilitarian attribute perceptions of the recipes suggested by the AI chatbot, as measured on a seven-point scale ranging from 1 = "mostly based on sensory pleasure (taste, aromas, etc.)" to 7 = "mostly based on healthy chemical properties (antioxidants, micro/macro nutrients, etc.)."

A one-way ANOVA on hedonic/utilitarian attribute perceptions revealed that the intervention attenuated the word-of-machine effect and led to higher hedonic perceptions compared to the control condition (M_intervention = 3.75, SD = 1.46, M_control = 4.25, SD = 1.37; F( 1, 297) = 9.15, p =.003; lower numbers indicate higher hedonic perceptions). These results corroborate those of Study 7a and provide evidence for a practical and relatively easier-to-implement intervention for managers looking to attenuate the lay belief underlying the word-of-machine effect.

As companies in the private and public sectors assess how to harness the potential of AI-driven recommendations, the question of how trade-offs in decision making influence preference for AI recommenders is of great importance. We address this question across nine studies and show a word-of-machine effect: the phenomenon by which hedonic and utilitarian trade-offs determine preference for (or resistance to) AI-driven recommendations. Studies 1a–1b show that a utilitarian (hedonic) goal makes people more (less) likely to choose AI recommenders than human ones. Study 2 shows that AI (human) recommenders lead to higher perceptions of utilitarian (hedonic) attributes upon consumption. Study 3 shows that people prefer AI (human) recommenders when utilitarian (hedonic) attributes are more important. Study 4 shows that differing competence perceptions underlie the word-of-machine effect and rule out complexity. Studies 5 and 6 identify boundary conditions: Study 5 shows that the word-of-machine effect is reversed for utilitarian goals if the recommendation needs to match a person's unique preferences, and Study 6 shows that the effect is eliminated when AI is framed as "augmented" rather than "artificial" intelligence, that is, in human–AI hybrid decision making. Finally, Studies 7a–7b tested an intervention to attenuate the word-of-machine effect.

Our research makes several important theoretical contributions. A first set of contributions speaks to research on the psychology of automation and on human–technology interactions ([12]; [17]; [30]). First, we extend this literature by addressing the question of whether hedonic/utilitarian trade-offs in decision making drive preference for or resistance to AI recommenders. This question is novel, as prior research has not relied on differences inherent to hedonic/utilitarian consumption to predict people's reactions to receiving advice from automated systems.

Second, we show under what circumstances AI-driven recommendations are preferred to, and therefore more effective, than human ones: when utilitarian attributes are relatively more important or salient than hedonic ones. Research in this area has largely focused on consumers' resistance to automated systems. For example, in the domain of performance forecasts, people are less likely to rely on the input of an algorithm than a person to make predictions about student performance, an effect that is due to the belief that algorithms, unlike people, cannot learn from their mistakes ([14]). In the domain of health care utilization, people are less likely to rely on an automated medical provider if a human provider is available, even when the two providers have the same accuracy ([27], [28]).

Limited research has identified under what circumstances resistance to algorithmic advice is attenuated: if people have the opportunity to modify algorithms and thus exert control over them ([15]), if the human likeness of algorithms is increased ([ 9]), if the task entails a numeric estimate of a target ([26]), and if the algorithm is described as tailoring a recommendation to a person's unique case ([27], [28]). We extend this literature by showing circumstances in which consumers' resistance to AI may be reversed and by showing cases in which consumers even prefer automated systems: when they assign greater importance to utilitarian attributes or when a utilitarian goal is activated.

Third, we explore under what circumstances consumers will be amenable to AI recommenders in the context of human–AI partnerships. We show that augmented intelligence helps bolster AI to the level of humans for hedonic decision making and helps bolster humans to the level of AI for utilitarian decision making. This contribution is important because it represents the first empirical test of augmented intelligence as an alternative conceptualization of artificial intelligence that focuses on AI's assistive role in advancing human capabilities. We hope that this contribution will prioritize new research focused on understanding the potential of AI in conjunction with humans rather than in contraposition, as this seems to be the advocated way forward by many practitioners ([ 3]; [18]).

We also contribute to the literature on hedonic and utilitarian consumption ([ 1]; [22]; [31]; [44]). Literature in this area has identified the factors that influence evaluation of hedonic and utilitarian product dimensions. We extend this literature by investigating how hedonic/utilitarian attribute trade-offs influence the effectiveness of a source of a product recommendation (i.e., a human vs. an AI recommender; Studies 1a, 1b, 3–5) and how the source of a product recommendation influences hedonic/utilitarian perceptions (Studies 2, 6–7b).

The current speed of development and adoption of AI, machine learning, and natural language processing algorithms challenge managers to harness these transformative technologies to optimize the customer experience. Our findings are insightful for managers as they navigate the remarkable technology-enabled opportunities that are growing in today's marketplace. These new technologies are also experiencing a renewed prominence in public discourse. For instance, the U.S. government has established the National Artificial Intelligence Research and Development Strategy to address economic and social implications of AI.

Our findings provide useful insights for both companies and public policy organizations debating if and how to effectively automate their recommendations systems. A company like Sephora relies both on human-based recommendations from sales associates and its customer base and AI-based recommendations through its Visual Artist app, a conversational bot that interacts with prospective shoppers. Our results suggest cases in which AI-based recommendations would be more effective (i.e., when utilitarian attributes are more salient or important, such as grooming products) and when they would be less effective (i.e., when hedonic attributes are more salient or important, such as fragrances).

Our results are insightful for strategic and tactical marketing decisions. Marketers could prioritize functional positioning strategies over experiential ones in the case of AI-based recommendations for target segments for whom utilitarian attributes are more important. For instance, a company in the hospitality industry such as TripAdvisor should emphasize AI-based recommendations for business travel services and deemphasize AI-based recommendations for leisure travel services. Our results also apply to a host of tactical decisions such as marketing communications. Managers could communicate to their customers in a way that is aligned with a target segment's goal (i.e., hedonic vs. utilitarian) and emphasize the most effective points of parity/difference with competing brands or across different products in the portfolio. Companies like Netflix and YouTube could emphasize AI-based recommendations when utilitarian attributes are relatively more important (e.g., documentaries) and human-based recommendations ("similar users") when hedonic attributes are relatively more important (e.g., horror movies).

This research also highlights boundary conditions that may prove useful for practitioners. Study 5 indicated that when consumers want recommendations that are matched to their unique preferences, they resist AI recommenders and instead prefer human recommenders, regardless of hedonic or utilitarian goals. These results suggest that companies whose customers are known to be satisfied with "one size fits all" recommendations, or who are not in need of a high level of customization, may rely on AI systems. However, companies whose customers are known to desire personalized recommendations should rely on humans. Some companies, such as Amazon, seem to be implementing a similar strategy. Even though most of Amazon's recommendations are based on algorithms, the company has recently started offering an additional service for an added fee called "personal shopper." This service relies on human shopping assistants to give clothing recommendations rather than on algorithms. Our results indicate that more companies, especially those in markets that are relatively more hedonic, should follow Amazon's example.

Study 6 provides another managerially relevant boundary condition: augmented intelligence. The results of this study indicate that consumers are more receptive to AI recommenders, even in the case of hedonic goals, if the AI recommender does not replace a human recommender but instead assists a human recommender who retains the role of ultimate decision maker. These results are important for practitioners managing relatively more hedonic products or services. For instance, in a personal conversation with the authors, a Walmart marketing manager noted how the top two most frequently ignored recommendations on the company's website are those for alcoholic beverages and food items—arguably products for which hedonic attributes tend to be more salient and important. In these circumstances, practitioners could leverage our results and utilize AI systems to generate an initial recommendation on which a human then "signs off."

Finally, in Studies 7a–7b we tested an intervention that practitioners managing relatively more hedonic products and relying on AI systems may execute. Building on the consider-the-opposite protocol, we created a realistic chatbot that interacted with participants and nudged them to consider that the AI recommender could be good at things that participants did not expect it to be good at. The intervention was successful in both studies, suggesting that practitioners may utilize this technique if hedonic attributes are important.

Despite the robustness of the word-of-machine effect, our research has limitations that offer several opportunities for future research. First, there is the possibility that drawing attention to the source of a recommendation primed study participants. AI recommenders might have primed utilitarian attributes or made utilitarian goals more salient, and it was the associated increased activation of these concepts, rather than competence perceptions, that gave rise to the word-of-machine effect. Although possible, this alternative explanation based on priming is unlikely given the results of a study we report in Web Appendix D. In this study (N = 230), we first primed participants with either human or AI-related concepts by drawing their attention to either a human or an AI recommender, thus approximating the kind of priming that could have occurred in our studies. To assess whether the AI recommender primed utilitarian concepts, we then measured perceptions of utilitarian and hedonic attributes of a stimulus in a domain unrelated to one in which the priming manipulation occurred. This stimulus was pretested to be neutral (i.e., perceived to be equally utilitarian and hedonic). The results indicate that the stimulus was perceived to be equally utilitarian and hedonic regardless of the priming manipulation. Although these results offer preliminary evidence that priming does not account for the word-of-machine effect, the inferences one can draw from a null effect are limited. More broadly, the question of whether AI-based recommendations activate specific constructs that might be influential on decision making is a worthy avenue for future research.

Second, even though we tested the word-of-machine effect across multiple domains, there remains the possibility that the effect is stronger or weaker in certain categories. For instance, the effect might be stronger in categories (e.g., a chocolate cake) in which discerning hedonic attributes (e.g., how tasty or how indulgent it is) is easier than discerning utilitarian attributes (e.g., how many macronutrients it contains, or how healthy it is). Future research could more systematically investigate what dimensions of different product categories strengthen versus weaken the word-of-machine effect.

Third, the lay beliefs underlying the word-of-machine effect may be transitional. As competence perceptions driving the word-of-machine effect are based on a lay belief, they are embedded in a cultural view that may change over time. The lay belief about differential competence perceptions may already be inaccurate, as AI is already utilized in domains that are relatively more hedonic. For instance, AI curates flower arrangements on the basis of customers' past transactions and inferred preferences (1-800-Flowers) and creates new flavors for food companies such as McCormick, Starbucks, and Coca-Cola ([41]).

Our research also suggests opportunities for future exploration of this area. First, the word-of-machine effect may have interesting downstream consequences on other responses. For instance, relying on an AI recommender may lead consumers to compensate by adjusting their own choices. Given the belief that AI-based recommendations excel on utilitarian attributes and are weaker on hedonic attributes, consumers may choose from a set of options by paying closer attention to the hedonic attributes of the options, assuming that the options are satisfactory in terms of utilitarian attributes. This "second-step choice" is an interesting question to consider in the future.

Second, in Studies 7a–b we show preliminary evidence of how lay beliefs toward AI systems could be successfully alleviated through a protocol utilized in the decision making literature. Future research could identify other real-world variables that might have similar attenuating effects, such as domain expertise, involvement, time spent making decisions, or familiarity/repeated use of AI systems. A third fruitful research opportunity would be to explore whether consumers can be persuaded to trust AI systems, even more than humans, in the eventuality that AI systems are sufficiently sophisticated to pass the Turing test. In this vein, future research could identify conditions under which the word-of-machine effect reverses, with AI recommenders being more persuasive than humans for hedonic products.

As research on the psychology of automation expands to include developments such as AI, we hope that our findings (especially those of Study 6) will spur further research prioritizing the understanding of the vast potential of AI operating in partnership with humans. More research is also necessary to map out the impact of AI systems across consumption settings. AI-powered technologies will be instrumental in optimizing the customer experience at each phase of the consumer journey by offering products of increasing personalization ([41]). New technologies like image, text, and voice recognition, together with large-scale A/B testing will provide managers with the data necessary for a complete, AI-driven customization of the journey ([41]) and will allow researchers to gather the consumer signals that are produced as a by-product of consumer activities ([38]). We hope that future research will focus on how to harness this great potential of AI for managers and researchers alike.

Overall, understanding when consumers will be amenable to and when they will resist AI-driven recommendations is a pressing and complex endeavor for researchers and firms alike. We hope that our research will spur further exploration of this important topic.

Supplemental Material, WOM_Web_Appendix_PDF - Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The "Word-of-Machine" Effect

Supplemental Material, WOM_Web_Appendix_PDF for Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The "Word-of-Machine" Effect by Chiara Longoni and Luca Cian in Journal of Marketing

1 All authors contributed equally.

2 Connie Pechmann

3 The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

4 The author(s) received no financial support for the research, authorship, and/or publication of this article.

5 Luca Cian https://orcid.org/0000-0002-8051-1366

6 https://doi.org/10.1177/0022242920957347

7 We thank the associate editor and two anonymous reviewers for this suggestion.

8 We added manipulation checks at the end of the survey. These manipulation checks were of recommender and goal, and participants indicated the recommender (human, AI) of the app they considered and the goal they had (hedonic, utilitarian). The recommender manipulation check was answered correctly by 93.0% of the participants, and the goal manipulation check was answered correctly by 90.8% of the participants. Statistical conclusions did not differ when restricting the analysis to participants who passed either manipulation check.

9 We thank an anonymous reviewer for this suggestion.

We validated this hypothesis by asking respondents from Amazon Mechanical Turk (N = 95) the extent to which they would expect a property selected by [a human/an AI] Realtor to match their unique preferences and needs (1 = "not at all," and 7 = "very much"). Indeed, participants expected the human Realtor to be more able than the AI Realtor to match a property recommendation to their unique preferences and needs (M_H = 5.85, SD = 0.82, M_AI = 4.70, SD = 1.30; F(1, 93) = 26.69, p <.001).

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By Chiara Longoni and Luca Cian

Reported by Author; Author

Augmented Reality in Retail and Its Impact on Sales 

The rise of augmented reality (AR) technology presents marketers with promising opportunities to engage customers and transform their brand experience. Although firms are keen to invest in AR, research documenting its tangible impact in real-world contexts is sparse. In this article, the authors outline four broad uses of the technology in retail settings. They then focus specifically on the use of AR to facilitate product evaluation prior to purchase and empirically investigate its impact on sales in online retail. Using data obtained from an international cosmetics retailer, they find that AR usage on the retailer's mobile app is associated with higher sales for brands that are less popular, products with narrower appeal, and products that are more expensive. In addition, the effect of AR is stronger for customers who are new to the online channel or product category, suggesting that the sales increase is coming from online channel adoption and category expansion. These findings provide converging evidence that AR is most effective when product-related uncertainty is high, demonstrating the technology's potential to increase sales by reducing uncertainty and instilling purchase confidence. To encourage more impactful research in this area, the authors conclude with a research agenda for AR in marketing.

Keywords: augmented reality; mobile app; online retail; product uncertainty; virtual product experience

"At some point, we're going to look back and think, how did

we not have a digital layer on the physical world?" – Greg Jones, Director of VR and AR at Google

Augmented reality (AR) is a technology that superimposes virtual objects onto a live view of physical environments, helping users visualize how these objects would fit into their physical world. Even though AR is in its early stages of growth, leaders in the field such as Apple's CEO, Tim Cook, and Google's Director of Virtual Reality (VR) and AR, Greg Jones, have lauded its potential to transform the retail experience ([ 3]; [23]). With the launch of AR toolkits by technology giants Apple and Google, it is now easier for companies to develop their own AR-enabled mobile apps. Jumping on the bandwagon, Facebook recently introduced AR-enabled display advertisements for their News Feed ([11]), making the technology even more accessible to companies.

Augmented reality (AR) is a technology that superimposes virtual objects onto a live view of physical environments, helping users visualize how these objects would fit into their physical world. Even though AR is in its early stages of growth, leaders in the field such as Apple's CEO, Tim Cook, and Google's Director of Virtual Reality (VR) and AR, Greg Jones, have lauded its potential to transform the retail experience ([ 3]; [23]). With the launch of AR toolkits by technology giants Apple and Google, it is now easier for companies to develop their own AR-enabled mobile apps. Jumping on the bandwagon, Facebook recently introduced AR-enabled display advertisements for their News Feed ([11]), making the technology even more accessible to companies.

From a retail perspective, a promising application of AR is to facilitate product evaluation by letting customers experience products virtually prior to purchase. Although research has emphasized the importance of direct product experiences to help customers learn about product benefits and assess product fit ([ 6]; [12]), offering direct product experiences can be a logistical challenge, especially in online retail. The introduction of AR has made it possible for shoppers to experience products virtually in the absence of physical products, managing their expectations and instilling purchase confidence ([44]). For example, Amazon and IKEA are using this technology to help customers determine if products or furniture pieces offered online are compatible with their existing room décor, and L'Oréal and Sephora are using AR to show customers how different cosmetic products would alter their appearance. Some of these applications are illustrated in Web Appendix A.

Despite the keen interest in AR, there has been limited research demonstrating its tangible impact in real-world contexts. Understanding the potential for AR to increase revenues is important for justifying investments in this new technology. However, the impact of AR on actual product sales is still ambiguous. By helping customers visualize products in their consumption contexts, AR could reduce product fit uncertainty, resulting in more sales. Conversely, AR may also discourage purchases if it leads to perceptions that the products may not fit well. As the technology is unable to convey experiential product attributes that could be important in purchase decisions (e.g., product texture or scent), the impact of AR on sales could also be insignificant. This uncertainty surrounding the impact of AR has been cited as one of the main reasons why companies are still hesitant to embrace the technology, even though most recognize the exciting opportunities it offers ([ 5]). Echoing this lack of clarity, a recent CNN article regarding applications of AR in the cosmetics industry expressed that "virtual lipsticks and smokey eye shadows are popular in apps, but are they translating into more makeup sales? Hard data isn't easy to come by" ([38]).

Furthermore, whether and how the impact of AR varies across different products or customer segments is also unclear. Having a more nuanced understanding of how AR affects sales would help marketing managers determine when it would be most appropriate to deploy the technology. Conceivably, if AR increases sales by reducing uncertainty, its impact may depend on product and customer characteristics that influence uncertainty in purchase decisions, such as brand popularity, product appeal, and customers' familiarity with the retail channel or category. Accordingly, the present research adopts the retailers' perspective to examine the following questions:

• How does the use of AR to facilitate product evaluation impact product sales?
• How does the sales impact of AR usage differ across product characteristics, such as brand popularity, product appeal, rating, and price?
• How do customers' prior experiences with the online channel and product category influence the sales impact of AR usage?

Given that AR is predominantly available on mobile apps ([43]), we focus on the mobile app platform for our analyses. We obtained data from an international cosmetics retailer that incorporated AR into its mobile app to help customers realistically visualize how they would look when they are using different cosmetic products (e.g., eyeshadows, lipsticks). The data contain sales records for 2,300 products, as well as browsing and purchase histories for 160,400 customers, allowing us to investigate how the sales impact of AR varies by product and customer characteristics. In addition, introduction of the AR feature for two product categories during the observation period provided us with a quasi-experimental setting to examine the impact of AR introduction on category sales.

Findings from our research provide preliminary evidence that AR usage has a positive impact on product sales. The overall impact appears to be small, but certain products are more likely to benefit from the technology than others. In particular, the impact of AR is stronger for brands that are less popular and products with narrower appeal, suggesting that AR could level the playing field for niche brands or products (sometimes referred to as products in the "long tail" of the product sales distribution; e.g., [ 8]). The increase in sales is also greater for products that are more expensive, indicating that AR could increase overall revenues for retailers. In addition, customers who are new to the online channel or product category are more likely to purchase after using AR, suggesting that AR has the potential to promote online channel adoption and category expansion. These findings provide converging evidence that AR is most effective when product-related uncertainty is high, implying that uncertainty reduction could be a possible mechanism by which AR could improve sales.

This article is one of the first to empirically demonstrate the impact of AR on sales and how it varies across product and customer characteristics using real-world data. In doing so, it extends prior studies on AR in the marketing field and represents an initial step in understanding what AR means for marketers and retailers. Beyond influencing sales, AR could transform the way brands reach out to and connect with customers at different stages of the customer journey. In the following section, we provide an overview of AR and elaborate on four ways the technology can be incorporated into brands' marketing strategies to reshape the customer retail experience. Then, we focus specifically on how the use of AR to facilitate product evaluation prior to purchase impacts sales in online retail. To encourage marketing academics to further engage in impactful and managerially relevant research in this area, we conclude with a research agenda that we developed in consultation with industry experts and marketing practitioners.

Augmented reality integrates virtual elements into real-world environments to create alternate perceptions of reality. Using sensors and object recognition capabilities from input devices such as cameras, AR technology scans the physical environment, identifies features in the environment, and superimposes virtual objects (e.g., two- or three-dimensional images or animations, text, sounds) on top of a live view of the real world. By blending virtual elements into physical environments in real time, AR enriches users' visual and auditory perceptions of reality. In most cases, the virtual elements are also responsive to movements or gestures, creating an interactive experience for users.

Although AR is often classified together with VR, the two technologies are distinct, both in how they function and the way they are experienced. Unlike AR, which receives input from the real world and adds virtual elements to it, VR immerses users in a completely digital and artificial environment, shutting them out from their surroundings. Due to the disorienting experience of being entirely isolated from the real world and the expensive headsets required ([19]), the appeal of VR has largely been limited to industries with products high in simulated content, such as gaming and entertainment ([13]). In contrast, AR allows users to experience virtual elements without the vulnerability of being blind to the real world. In addition, AR can be experienced directly from handheld devices that users already own (e.g., tablets or smartphones). Thus, AR is rapidly gaining prominence, and close to 100 million U.S. consumers are expected to use the technology regularly by 2022 ([43]).

The unique capabilities of AR present marketers with new opportunities to engage customers and transform the brand experience. Drawing on an extensive review of current applications of AR, we identified four broad uses of the technology in retail settings: to ( 1) entertain and ( 2) educate customers, help them ( 3) evaluate product fit, and ( 4) enhance the postpurchase consumption experience. These uses loosely correspond to customers' journey from awareness to interest, consideration, purchase, and consumption, and they may not be mutually exclusive. Next, we elaborate on these four uses and provide a summary with relevant examples in Table 1.[ 6]

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Table 1. Uses of AR in Retail.

1 Note: URL links to these examples are provided in Web Appendix B.

AR's ability to transform static objects into interactive and animated three-dimensional objects offers new ways for marketers to create fresh experiences to captivate and entertain customers. Besides generating hype and interest, marketers have also used AR-enabled experiences to drive traffic to their physical locations. For example, Walmart collaborated with media companies such as DC Comics and Marvel to bring exclusive superhero-themed AR experiences to their stores by placing special thematic displays in selected outlets. In addition to creating novel and engaging experiences for customers, it also encouraged them to explore different areas within the stores.

Due to its interactive and immersive format, AR is also an effective medium for delivering content and information to customers. For instance, to help customers better appreciate their new car models, Toyota and Hyundai have utilized AR to demonstrate key features and innovative technologies in a vivid and visually appealing manner. Retailers can also use AR to help customers navigate stores or highlight relevant product information to influence their in-store purchase decisions. Companies such as Walgreen's and Lowe's have developed in-store navigation apps that overlay directional signals onto a live view of the path in front of users to guide them to product locations and notify them if there are special promotions along the way.

By retaining the physical environment as a backdrop to virtual elements, AR also helps users visualize how products would appear in their actual consumption contexts, allowing them to more accurately assess product fit prior to purchase. For example, IKEA's Place app uses AR to give customers a preview of different furniture pieces in their homes by overlaying true-to-scale, three-dimensional models of products onto a live view of the room. Customers can easily determine if a product fits in a given space without the hassle of taking measurements. Fashion retailers Uniqlo and Topshop have also deployed the same technology in their physical stores, offering customers greater convenience by reducing the need for them to change in and out of different outfits. An added advantage of AR is its ability to accommodate a wide assortment of products. By replacing tangible product displays with lifelike virtual previews of products, retailers can overcome the constraints of physical space while still offering customers the opportunity to explore different product options. This capability is particularly useful for made-to-order or bulky products. Car manufacturers BMW and Audi have used AR to provide customers with true-to-scale, three-dimensional visual representations of car models based on customizable features such as paint color, wheel design, and interior aesthetics. These cases exemplify AR's huge potential to increase customers' confidence in their purchase decisions for a variety of products.

Lastly, AR can be used to enhance and redefine the way products are experienced or consumed after they have been purchased. For example, LEGO recently launched several brick sets that are specially designed to combine physical and virtual gameplay. Through the companion AR app, animated LEGO characters spring to life and interact with the physical LEGO sets, creating a whole new playing experience. In a bid to address skepticism about the quality of its food ingredients, McDonald's has also used AR to let customers discover the origins of ingredients in the food they purchased via storytelling and three-dimensional animations.

The present research focuses on the use of AR to help customers evaluate products prior to purchase. Specifically, we explore the possibility of leveraging AR to reduce product-related uncertainty in online purchase decisions. To extend prior research on AR in retail (summarized in Table 2), we use real-world data to examine how customers' use of AR to try products (for brevity, we refer to this as "AR usage" for the rest of the article) affects product and brand sales. In the following section, we present our conceptual framework and develop hypotheses for the impact of AR usage on sales.

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Table 2. Selected Literature on AR in Retail.

Because customers cannot perfectly predict the consequences of their purchase decisions, uncertainty is inherent in market exchanges ([ 4]). However, it is especially pronounced in online environments due to the spatial separation between buyers and sellers as well as the temporal separation between payment and product fulfillment ([ 9]; [41]). Unlike in traditional retail settings, customers are unable to physically inspect or evaluate products before making a purchase, resulting in greater uncertainty that the products would be able to deliver the expected level of performance or benefits ([ 6]; [17]; [36]).

Researchers have broadly distinguished between two types of product uncertainty in online markets: product performance uncertainty and product fit uncertainty. Product performance uncertainty occurs when customers are unable to evaluate or predict product performance due to imperfect knowledge ([17]). In contrast, product fit uncertainty occurs when customers are unable to determine if the product matches their needs ([ 6]; [32]). The latter form of uncertainty is typically higher for products with experience attributes (i.e., attributes that can only be evaluated after the product has been experienced; [32]), such as apparel or beauty products.

Several mechanisms to reduce product performance uncertainty in online retail have been suggested. For example, retailers could lower information asymmetry by providing diagnostic product descriptions or by including credibility signals such as third-party product assurances, warranties, or customer reviews ([17]; [51]). In contrast, product fit uncertainty typically requires direct product experience to resolve, as it is idiosyncratic in nature and varies from individual to individual. Although some retailers have adopted try-before-you-buy programs (e.g., Warby Parker's home try-on program; [ 6]) or lenient product return policies ([24]; [53]) to provide opportunities for direct product experiences, these measures are notoriously costly for retailers due to the additional shipping and handling costs and risks of product damage ([22]). Furthermore, direct product experiences may not be viable or appropriate for certain products, such as products that are customized (e.g., engagement rings), products that require assembly (e.g., furniture), or personal care products (e.g., cosmetics).

The introduction of AR has made it possible to substitute direct product experiences with virtual product experiences to facilitate product evaluation and reduce product fit uncertainty. Using a situated cognition perspective, [29] propose that the value of AR lies in its ability to help customers visually integrate virtual products into the real-world environment (i.e., "environmental embedding") and use bodily movements and physical actions to control how products are presented (i.e., "simulated physical control"). The unique combination of these two properties induces perceptions that the virtual products are physically present in the real world, creating realistic product experiences. Consequently, customers are able to evaluate products as if they are actually interacting with the real products, resulting in reduced product fit uncertainty. In line with this, prior research finds that vivid images and greater control over the presentation of information are effective ways to alleviate uncertainty in online environments ([51]). By helping customers visualize products in their consumption contexts and reducing product fit uncertainty, AR-enabled product experiences increase the level of ease customers feel in the decision-making process, translating to positive behavioral intentions ([26]; [29]).

However, although AR communicates visual information about products, it is unable to convey other experiential product attributes (e.g., product texture, scent). For example, even though customers may use AR to visualize an IKEA sofa in a room, they are unable to assess how comfortable it is. Similarly, users trying on cosmetic products via AR are unable to evaluate other product attributes such as the texture and consistency of the product, which may affect ease of application and the way the product feels on the skin. According to [34], if customers do not perceive trial experiences as accurately representing actual consumption experiences, they may discount those trial experiences when they form judgments about the product. Thus, the extent to which virtual product experiences involving AR could influence online purchases is unclear. Nevertheless, as prior research has demonstrated the positive effects of providing fit information in online retail (e.g., [21]; [35]), we expect AR usage to have a positive impact on product sales because the technology could convey visual information that may reduce product fit uncertainty in online purchase decisions. Therefore, we predict the following:

• H1: AR usage has a positive impact on sales.

Building on the proposition that AR usage increases sales by reducing product fit uncertainty, we further hypothesize that AR would have a stronger impact when customers experience higher levels of uncertainty. In particular, the level of uncertainty experienced in a purchase decision could depend on product characteristics such as brand popularity, product appeal, and ratings. The level of uncertainty may also influence the price that customers are willing to pay for the product. Thus, the relationship between AR usage and sales may differ across these product characteristics. In addition, customers also vary in their need to reduce product fit uncertainty before making a purchase ([ 6]). This need to reduce uncertainty could depend on customers' familiarity with the online channel and product category. As a result, the impact of AR may also vary across these customer characteristics. Accordingly, we develop hypotheses for the moderating effects of product and customer characteristics in the following sections. Our conceptual framework is presented in Figure 1.

Graph: Figure 1. Conceptual framework.Note: Signs in parentheses represent the hypothesized effects.

Prior research has shown that consumers are more cautious when they purchase from lesser-known brands, as they anticipate feeling more regret if the product turns out to be inferior ([45]). Consistent with this, [18] find that cultures high in uncertainty avoidance place greater emphasis on brand credibility. In online environments, brand signals are even more important because consumers are not able to inspect products before purchasing ([16]). However, [31] demonstrates that when additional information is available to facilitate decision making, consumers rely less on brand signals. As a result, less-established brands benefit more from the increased availability of information. In the same vein, by communicating visual information to help customers assess product fit, AR may reduce uncertainty in online purchase decisions. Consequently, AR may decrease customers' reliance on brand signals and inadvertently increase preference for brands that are less popular. We use the term "popular" in a general sense to refer to brands that are more widely adopted. Therefore, we hypothesize the following:

• H2a: The impact of AR usage on sales is stronger for brands that are less popular.

Within the same category or brand, products may also have different levels of appeal due to the alignment between their inherent characteristics and general consumer preferences. For example, a red lipstick is more mainstream and has broader appeal than a blue lipstick. We draw a distinction between brand popularity and product appeal in that the latter depends on intrinsic properties of the product and could be independent of the brand. Thus, a red lipstick from an unknown brand could have broad appeal but low brand popularity, whereas a blue lipstick from a well-known brand could have limited appeal despite having high brand popularity. As products with broad appeal cater to the masses, they are more likely to match the needs of the general consumer. Conversely, because products with narrower appeal serve a niche segment, there is a higher probability that they will not match the preferences of the general consumer and will thus carry greater product fit uncertainty. Nevertheless, [ 8] demonstrate that in online contexts, search and discovery features such as search tools or recommendation engines can shift consumers' preferences to niche products by lowering the cost of acquiring product information. Consistent with this, [50] find that products with narrower appeal benefit more from greater information availability. By visually conveying product information to help customers assess product fit in an effortless and risk-free environment, AR could have a stronger impact for products with narrower appeal due to the higher product fit uncertainty associated with these products. Therefore, we hypothesize the following:

• H2b: The impact of AR usage on sales is stronger for products with narrower appeal.

Customers often turn to online ratings or reviews as a source of information to resolve uncertainty about product quality and fit ([14]). In line with this, [37] find that consumers from countries that are high in uncertainty avoidance are more sensitive to both the valence and volume of product ratings. However, as consumers tend to overrate direct experiences with products ([30]), the ability to evaluate products and resolve uncertainty via firsthand experiences with those products on AR platforms may reduce customers' reliance on online ratings. Thus, by enabling customers to learn about product benefits and assess product fit through their own virtual experiences, AR could diminish the role of online ratings in purchase decisions. As a result, customers may be more amenable to purchasing products despite their lower ratings if they are able to try these products using AR. Therefore, we predict the followsing:

• H2c: The impact of AR usage on sales is stronger for products with lower ratings.

When customers experience product uncertainty, they are not able to accurately assess the benefits the products offer. As a result, customers are more likely to undervalue the products and are less willing to pay a premium ([17]). Consistent with this, [36] find that customers who are familiar with online shopping are still hesitant to purchase expensive products through the internet when there is a high degree of product uncertainty because they could suffer greater financial losses if these products do not fit them well. By facilitating product evaluation prior to purchase, AR helps customers ascertain if products match their needs and preferences. Consequently, customers may experience less uncertainty and feel more comfortable purchasing products that are more expensive. In line with this, [27] find that AR usage improves decision comfort, leading to higher willingness to pay. Therefore, we predict the following:

• H2d: The impact of AR usage on sales is stronger for more expensive products.

According to [36], customers who are familiar with online shopping are more inclined to purchase products with a higher degree of uncertainty because their cumulative online shopping experiences help them develop the ability to assess products when limited information is available. Thus, customers who have purchased from a retailer's online channel in the past may feel more comfortable making subsequent online purchases despite experiencing product uncertainty, potentially making them less dependent on AR to make their purchase decisions. In contrast, customers who are new to the retailer's online channel (but have made prior purchases at the retailer's offline channel) are not accustomed to making purchases in the absence of actual products. As a result, they may experience greater product fit uncertainty and may be deterred from purchasing online due to the inability to assess product fit. Because AR simulates the in-store experience of trying products, it may help reduce product fit uncertainty for customers who are new to the online channel. These customers may derive greater value from the ability to evaluate products virtually, potentially making them more likely to purchase online after using AR. Therefore, we predict the following:

• H3a: The impact of AR usage on sales is stronger for customers who are new to the retailer's online channel.

Besides channel experience, customers' familiarity with the product category also affects their level of product fit uncertainty ([32]). Customers who are familiar with a product category can draw on their prior experiences as an information source to form judgments about products ([46]). As a result, they may rely less on AR in their purchase decisions. Conversely, customers who are unfamiliar with a product category lack the necessary category knowledge to evaluate product attributes and, at the same time, may not be aware of their own preferences ([32]). Consequently, these customers will have more difficulty assessing whether a product's attributes match their preferences, resulting in greater product fit uncertainty. By helping customers visualize how products would appear in their actual consumption contexts, AR could reduce product fit uncertainty and increase purchase confidence for customers who are new to the product category. As a result, AR usage may have a stronger impact on the purchase decisions for these customers. Therefore, we predict the following:

• H3b: The impact of AR usage on sales is stronger for customers who are new to the product category.

To summarize, we propose that AR usage will positively impact sales by reducing product uncertainty. Following this line of reasoning, we developed several predictions about which products would be more likely to benefit from AR and which customers would be more likely to respond to AR.

As AR is predominantly available through mobile apps ([43]), we focus our analyses on the mobile app platform. To test our hypotheses, we obtained data from an international cosmetics retailer with both an online and offline presence. Leveraging AR technology, the retailer integrated a new feature on its existing mobile app that allowed customers to virtually try on makeup products (e.g., eyeshadows, lipsticks). The AR technology detected customers' facial features via their smartphone cameras and superimposed the shade of chosen products onto a live view of their face in real time, giving them a realistic visual representation of their appearance when they are using the products. The brand, product name, and price were displayed at the top of the screen. Figure A3 in Web Appendix A provides a visual example of a customer trying on a lipstick using the AR feature. For comparison, the corresponding product detail page view (i.e., the conventional way of conveying product-related information on mobile retail apps) is also provided. Prior to the start of our observation period in December 2017, the AR feature was only available for lip categories (i.e., lipstick and lip gloss) and was subsequently introduced for eye categories (i.e., eyeshadow and eyeliner) in March 2018. Figure A4 in Web Appendix A provides a visual overview of AR availability for the different categories.

We obtained two separate data sets from the retailer for one of its key markets in Asia Pacific. The first data set contained information about browsing activities on the mobile app, including specific products customers tried using the AR feature, and covered a 19-month period from December 2017 to June 2019. The second data set contained transaction records from June 2017 to June 2019 for all retail channels, including mobile app, website, and offline stores. We merged the two using customers' loyalty card number, which allowed us to match AR usage and product purchases at a disaggregate level.

During the 19-month period, a total of 160,407 shoppers browsed products from the lip and eye categories across 806,029 sessions, 20.8% of which involved AR usage. Customers who used AR during the session spent 20.7% more time browsing (M_{with_AR} = 16.6 minutes, M_{without_AR} = 13.8 minutes, p <.01) and browsed 1.28 times more products (M_{with_AR} = 53.9, M_{without_AR} = 42.2, p <.01). The purchase rate for sessions with AR usage was 19.8% higher than for sessions without AR usage (3.15% with AR vs. 2.63% without AR, p <.01), providing preliminary indication of the positive impact of AR on sales.

We divide our analyses into three sections. In the first section, we perform the analysis at the product level to examine the moderating effects of brand popularity, product appeal, rating, and price. To minimize selection bias arising from availability of the AR feature, we focus on lipsticks and lip glosses, as the feature was available for > 96% of products in each of these categories. In the second section, we take advantage of the introduction of AR for two eye categories (i.e., eyeshadow and eyeliner) to examine the effect of AR introduction on category sales using a quasi-experimental differences-in-differences-in-differences (DDD) approach. Finally, we investigate how the impact of AR varies at the customer level. As all customers had no knowledge that the AR feature would be introduced for the two eye categories prior to the introduction, the event provided us with a clean setting for examining how customers' channel and category experience (prior to the introduction) would moderate the impact of AR usage on purchase probability.

As product color is an important factor in cosmetic purchases, we considered each shade/color of retail merchandise as a unique product. In total, we had 2,334 products in the lipstick and lip gloss categories (1,984 products across 41 brands for lipstick; 350 products across 28 brands for lip gloss). Our empirical strategy was to relate the number of customers using AR to try each product during a particular time period with sales volume for that product during the same time period. We estimated the model at the monthly product level, giving us a total of 44,346 observations (2,334 products × 19 months from December 2017 to June 2019). As one of our objectives was to examine the moderating effect of product ratings, we included products with a rating in the main analysis and replicated the analysis for all products as a robustness check. Our final sample for the main analysis consisted of 29,345 observations.

For each product i, we modeled how the volume of AR usage in month t, AR Usage_it, influenced the number of products sold in month t, Product Sales_it. As Product Sales_it was a count variable with significant over-dispersion (M =.46, SD = 1.73) and over 80% of observations were 0, we used a zero-inflated negative binomial model for the estimation. The vector of covariates in the regression is given by the following equation:

$\begin{array}{c}{X}_{it}\beta ={\text{β}}_0+{\text{ β}}_1{\text{AR Usage}}_{\text{it}}+{\text{ β}}_2{\text{Brand Popularity}}_{\text{it}}\\ +{\text{ β}}_3{\text{Appeal}}_{\text{it}}+{\text{β}}_4{\text{Rating}}_{\text{it}}+{\text{β}}_5{\text{Price}}_{\text{it}}\\ +{\text{ β}}_6{\text{AR Usage}}_{\text{it}}\times {\text{Brand Popularity}}_{\text{it}}\\ +{\text{ β}}_7{\text{AR Usage}}_{\text{it}}\times {\text{Appeal}}_{\text{it}}\\ +{\text{ β}}_8{\text{AR Usage}}_{\text{it}}\times {\text{Rating}}_{\text{it}}\\ +{\text{ β}}_9{\text{AR Usage}}_{\text{it}}\times {\text{Price}}_{\text{it}}\\ +{\text{ β}}_{10}{\text{Category}}_{\text{i}}+{\displaystyle \sum }_{\text{m }=1}^{\text{T }-1}{\text{δ}}_{\text{m}}{\text{Month}}_{\text{t}}+{\text{∊}}_{\text{it}}.\end{array}$

Graph

1

In Equation 1, we measured AR Usage_it as the number of customers using AR to try product i during month t. As brands that are more widely adopted should have higher sales, and because the web and app channels are both online and carry identical products, we used total brand sales (within the category) from the web channel during the same period as a proxy for brand popularity, Brand Popularity_it. Following prior research using product sales as an indicator of mass or niche appeal (e.g., [ 8]), we used total product sales from the web channel during the same period to reflect product i's breadth of appeal, Appeal_it. Rating_it and Price_it were the rating and price of product i at time t, respectively. To examine how the impact of AR was influenced by brand popularity, product appeal, rating, and price, we included the corresponding interactions in the model. In addition, we included Category_i (1 = lipstick, 0 = lip gloss) and a series of dummy variables, Month_t, (for t = 1,..., T months) to control for category and month effects. Table 3 provides a summary of how the variables were operationalized and their descriptive statistics, and we provide their correlations in Web Appendix C. All the correlations were low, and the variance inflation factors were below 1.62, indicating that multicollinearity was not an issue. To prevent overestimation of effects due to the panel structure of the data, we clustered standard errors at the product level (e.g., [49]).

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Table 3. Variable Operationalization and Descriptive Statistics for Product Model.